Introduction

About this handbook

About this handbook

Objective

To make an open-access digital R reference book which is catered to epidemiologists and public health practitioners, usable offline, and addresses common epidemiological tasks via clear text explanations, step-by-step instructions, and best practice R code examples

The problem:

Most online R help resources are not task-centered nor epidemiology-focused. Epis learning or new to R often must Google and skim dozens of forum pages to complete common data manipulation and visualization epi tasks. Furthermore, field epidemiologists often work in low internet-connectivity environments and have limited technical support.

How to read this handbook:

• This handbook in an HTML file. It is not online, you are only using your web browser to view this local file.

• This handbook is best viewed with Google Chrome. Some functions may not work in other browsers.

• Use tabs on the right to hide/view code. See ‘Copy to clipboard’ icon in the upper-right of each code section

Version
The latest version of this handbook can be found at this github repository.

Style

• The handbook gives one recommended way of completing a task, and offers other methods/packages as appropriate.
  
• The handbook generally uses tidyverse R codying style. Read more here (TODO).
• Package names are written in bold (e.g. dplyr) and functions are like this: mutate().
  
• Code in this handbook sometimes explicity names the package of a function (e.g. dplyr::mutate()), so it is clear to the reader which package is being used.

Types of notes

FOR EXAMPLE: This is a boxed example

NOTE: This is a note

TIP: This is a tip.

CAUTION: This is a cautionary note.

DANGER: This is a warning.

Datasets used

Datasets used

Here the datasets used in this handbook will be described and will be “downloadable” via link (the files will be stored within the HTML, so available offline as well)

• Linelist (…) Linelist for the 2013 (first wave) H7N9 outbreak in China (source)
• Aggregated case counts (…)
  
• GIS coordinates (…)
  
• GIS shapefile (…)
  
• modeling dataset? (…)

Contributors

Contributors

Editor-in-Chief: Neale Batra (neale.batra@gmail.com)

Editorial core team: …

Authors: …

Reviewers: …

Advisors …

Data contributors:
outbreaks package

Some of this material comes from the R4Epis website, which was also made by some of the same people…

RECON packages

Photo credits (logo): CDC Public Image gallery; R Graph Gallery

R Basics

Overview

This section is not meant as a comprehensive “how to learn R” tutorial. However, it does cover some of the fundamentals that can be good to reference or refresh.

More comprehensive tutorials are available online:
* Here
* and Here
* and even Here
* Oh yea and Here too (there’s a lot of them)

Why use R?

Why use R?

• Reproducibility
  
• Fewer errors
  
• Collaboration
  
• Free

Installation

Installation

How to install R

How to install R Studio

Other things you may need to install:
* TinyTeX
* Pandoc
* RTools

RStudio

RStudio

RStudio Orientation

First, open RStudio. As their icons can look very similar, be sure you are opening RStudio and not R.

For RStudio to function you must also have R installed on the computer (see this section for installation instructions).

RStudio is an interface (GUI) for easier use of R. You can think of R as being the engine of a vehicle, doing the crucial work, and RStudio as the body of the vehicle (with seats, accessories, etc.) that helps you actually use the engine to move forward!

By default RStudio displays four rectangle panes.

TIP: If your RStudio displays only one left pane it is because you have no scripts open yet.

The R Console Pane

The R Console, by default the left or lower-left pane in R Studio, is the home of the R “engine”. This is where the commands are actually run and non-graphic outputs and error/warning messages appear. You can directly enter and run commands in the R Console, but realize that these commands are not saved as they are when running commands from a script.

If you are familiar with Stata, the R Console is like the Command Window and also the Results Window.

The Source Pane
This pane, by default in the upper-left, is space to edit and run your scripts. This pane can also display datasets (data frames) for viewing.

For Stata users, this pane is similar to your Do-file and Data Editor windows.

The Environment Pane
This pane, by default the upper-right, is most often used to see brief summaries of objects in the R Environment in the current session. These objects could include imported, modified, or created datasets, parameters you have defined (e.g. a specific epi week for the analysis), or vectors or lists you have defined during analysis (e.g. names of regions). Click on the arrow next to a dataframe name to see its variables.

In Stata, this is most similar to Variables Manager window.

Plots, Packages, and Help Pane
The lower-right pane includes several tabs including plots (display of graphics including maps), help, a file library, and available R packages (including installation/update options).

This pane contains the Stata equivalents of the Plots Manager and Project Manager windows.

RStudio settings

Change RStudio settings and appearance in the Tools drop-down menu, by selecting Global Options

Scripts

Scripts

Why use a script

R scripts (vs. typing in the console)
* Advantages (reproducability) * General sequence (into, load packages, load data, clean data, conduct analysis, save results) * Commenting

Rmarkdown

R notebooks

RShiny

Working directory

Working directory

These tabs cover how to use R working directories, and how this changes when you are working within an R project. The working directory is the root file location used by R for your work.
By default, it will save new files and outputs to this location, and will look for files to import (e.g. datasets) here as well.

NOTE: If using an [R project](#rproject), the working directory will default to the R project root folder **IF** you open RStudio by clicking open the R project (the file with .rproj extension))

Set by Command

Use the command setwd() with the filepath in quotations, for example: setwd("C:/Documents/R Files")

CAUTION: If using an RMarkdown script be aware of the following:

In an R Markdown script, the default working directory is the folder the Rmarkdown file (.Rmd) is saved to. If you want to change this, you can use setwd() as above, but know the change will only apply to that specific code chunk.

To change the working directory for all code chunks in an R markdown, edit the setup chunk to add the root.dir = parameter, such as below:

knitr::opts_knit$set(root.dir = 'desired/filepath/here')

Set Manually

Setting your working directory manually (point-and-click)

From RStudio click: Session / Set Working Directory / Choose Directory (you will have to do this each time you open RStudio)

In an R project

How things change in an R project

Objects

Objects

Everything in R is an object. These sections will explain:

• How to create objects (<-)
• Types of objects (e.g. data frames, vectors..)
  
• How to access subparts of objects (e.g. variables in a dataset)
  
• Classes of objects (e.g. numeric, character, factor)

Everything is an object

Everything you store in R - datasets, variables, a list of village names, a total population number, even outputs such as graphs - are objects which are assigned a name and can be referenced in later commands.

An object exists when you have assigned it a value (see the assignment section below). When it is assigned a value, the object appears in the Environment (see the upper right pane of RStudio). It can then be operated upon, manipulated, changed, and re-defined.

Creating objects (<-)

Create objects by assigning them a value with the <- operator.
You can think of the assignment operator <- as the words “is defined as”. Assignment commands generally follow a standard order:

object_name <- value (or process/calculation that produce a value)

EXAMPLE: You may want to record the current epidemiological reporting week as an object for reference in later code. In this example, the object reporting_week is created when it is assigned the character value "2018-W10" (the quote marks make these a character value).
The object reporting_week will then appear in the RStudio Environment pane (upper-right) and can be referenced in later commands.

See the R commands and their output in the boxes below.

reporting_week <- "2018-W10"   # this command creates the object reporting_week by assigning it a value
reporting_week                 # this command prints the current value of reporting_week object in the console
## [1] "2018-W10"

NOTE: Note the [1] in the R console output is simply indicating that you are viewing the first item of the output

CAUTION: An object’s value can be over-written at any time by running an assignment command to re-define its value. Thus, the order of the commands run is very important.

The following command will re-define the value of reporting_week:

reporting_week <- "2018-W51"   # assigns a NEW value to the object reporting_week
reporting_week                 # prints the current value of reporting_week in the console
## [1] "2018-W51"

Datasets are also objects and must be assigned names when they are imported.

In the code below, the object linelist is created and assigned the value of a CSV file imported with the rio package.

# linelist_raw is created and assigned the value of the imported CSV file
linelist <- rio::import("my_linelist.csv")

You can read more about importing and exporting datasets with the section on importing data.

CAUTION: A quick note on naming of objects:

• Object names must not contain spaces, but you should use underscore (_) or a period (.) instead of a space.
  
• Object names are case-sensitive (meaning that Dataset_A is different from dataset_A).
• Object names must begin with a letter (cannot begin with a number like 1, 2 or 3).

Object Structure

Objects can be a single piece of data (e.g. my_number <- 24), or they can consist of structured data.

The graphic below, sourced from this online R tutorial shows some common data structures and their names. Not included in this image is spatial data, which is discussed in the GIS section.

In epidemiology (and particularly field epidemiology), you will most commonly encounter data frames and vectors:

Common structure	Explanation	Example from templates
Vectors	A container for a sequence of singular objects, all of the same class (e.g. numeric, character).	“Variables” (columns) in data frames are vectors (e.g. the variable age_years).
Data Frames	Vectors (e.g. columns) that are bound together that all have the same number of rows.	linelist_raw and linelist_cleaned are both data frames.

Note that to create a vector that “stands alone”, or is not part of a data frame (such as a list of location names), the function c() is often used:
list_of_names <- c("Ruhengeri", "Gisenyi", "Kigali", "Butare")

Object Classes

All the objects stored in R have a class which tells R how to handle the object. There are many possible classes, but common ones include:

Class	Explanation	Examples
Character	These are text/words/sentences “within quotation marks”. Math cannot be done on these objects.	“Character objects are in quotation marks”
Numeric	These are numbers and can include decimals. If within quotation marks the will be considered character.	23.1 or 14
Integer	Numbers that are whole only (no decimals)	-5, 14, or 2000
Factor	These are vectors that have a specified order or hierarchy of values	Variable msf_involvement with ordered values N, S, SUB, and U.
Date	Once R is told that certain data are Dates, these data can be manipulated and displayed in special ways. See the page on Dates for more information.	2018-04-12 or 15/3/1954 or Wed 4 Jan 1980
Logical	Values must be one of the two special values TRUE or FALSE (note these are not “TRUE” and “FALSE” in quotation marks)	TRUE or FALSE
data.frame	A data frame is how R stores a typical dataset. It consists of vectors (columns) of data bound together, that all have the same number of observations (rows).	The example AJS dataset named linelist_raw contains 68 variables with 300 observations (rows) each.

You can test the class of an object by feeding it to the function class(). Note: you can reference a specific column within a dataset using the $ notation to separate the name of the dataset and the name of the column.

class(linelist$age)     # class should be numeric
## [1] "numeric"

class(linelist$gender)  # class should be character
## [1] "character"

Often, you will need to convert objects or variables to another class.

Function	Action
as.character()	Converts to character class
as.numeric()	Converts to numeric class
as.integer()	Converts to integer class
as.Date()	Converts to Date class - Note: see section on dates for details
as.factor()	Converts to factor - Note: re-defining order of value levels requires extra arguments

Here is more online material on classes and data structures in R.

Variables ($)

Vectors within a data frame (variables in a dataset) can be called, referenced, or created using the $ symbol. The $ symbol connects the name of the column to the name of its data frame. The $ symbol must be used, otherwise R will not know where to look for or create the column.

# Retrieve the length of the vector age_years
length(linelist$age) # (age is a variable in the linelist data frame)

By typing the name of the data frame followed by $ you will also see a list of all variables in the data frame. You can scroll through them using your arrow key, select one with your Enter key, and avoid spelling mistakes!

knitr::include_graphics(here::here("images", "Calling_Names.gif"))

ADVANCED TIP: Some more complex objects (e.g. an epicontacts object may have multiple levels which can be accessed through multiple dollar signs. For example epicontacts$linelist$date_onset) .

Access/View with brackets ([])

You may need to view parts of objects, which is often done using the square brackets [ ].

To view specific rows and columns of a dataset, you can do this using the syntax dataframe[rows, columns]:

# View a specific row (2) from dataset, with all columns
linelist[2,]

# View all rows, but just one column
linelist[, "date_onset"]

# View values from row 2 and columns 5 through 10
linelist[2, 5:10] 

# View values from row 2 and columns 5 through 10 and 18
linelist[2, c(5:10, 18)] 

# View rows 2 through 20, and specific columns
linelist[2:20, c("date_onset", "outcome", "age")]

# View rows and columns based on criteria
# *** Note the dataframe must still be names in the criteria!
linelist[linelist$age > 25 , c("date_onset", "date_birth", "age")]

# Use View() to see the outputs in the RStudio Viewer pane (easier to read) 
# *** Note the capital "V" in View() function
View(linelist[2:20, "date_onset"])

# Save as a new object
new_table <- linelist[2:20, c("date_onset")] 

The square brackets also work to call specific parts of summary() function:

# All of the summary
summary(linelist$age)
##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max.    NA's 
##    2.00   47.25   60.00   56.91   72.00   91.00       2

#Just one part
summary(linelist$age)[2]
## 1st Qu. 
##   47.25

Functions

Functions

This section on functions explains:
* What a function is and how they work
* What arguments are
* What packages are
* How to get help understanding a function

Simple Functions

A function is like a machine that receives inputs, does some action with those inputs, and produces an output.
What the output is depends on the function.

Functions typically operate upon some object placed within the function’s parentheses. For example, the function sqrt() calculates the square root of a number:

sqrt(49)
## [1] 7

Functions can also be applied to variables in a dataset. For example, when the function summary() is applied to the numeric variable age in the dataset linelist (what’s the $ symbol?), the output is a summary of the variable’s numeric and missing values.

summary(linelist$age)
##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max.    NA's 
##    2.00   47.25   60.00   56.91   72.00   91.00       2

NOTE: Behind the scenes, a function represents complex additional code that has been wrapped up for the user into one easy command.

Functions with Multiple Arguments

Functions often ask for several inputs, called arguments, located within the parentheses of the function, usually separated by commas.

• Some arguments are required for the function to work correctly, others are optional.
  
• Optional arguments have default settings if they are not specified.
• Arguments can take character, numeric, logical (TRUE/FALSE), and other inputs.

For example, this age_pyramid() command produces an age pyramid graphic based on defined age groups and a binary split variable, such as gender. The function is given three arguments within the parentheses, separated by commas. The values supplied to the arguments establish linelist as the data frame to use, age_group as the variable to count, and gender as the binary variable to use for splitting the pyramid by color.

NOTE: For this example, in the background we have created a new variable called “age_group”. To learn how to create new variable see that section of this handbook

# Creates an age pyramid by specifying the dataframe, age group variable, and a variable to split the pyramid
apyramid::age_pyramid(data = linelist, age_group = "age_group", split_by = "gender")

The first half of an argument assignment (e.g. data =) does not need to be specified if the arguments are written in a specific order (specified in the function’s documentation). The below code produces the exact same pyramid as above, because the function expects the argument order: data frame, age_group variable, split_by variable.

# This command will produce the exact same graphic as above
apyramid::age_pyramid(linelist, "age_group", "gender")

A more complex age_pyramid() command might include the optional arguments to:

• Show proportions instead of counts (set proportional = TRUE when the default is FALSE)
  
• Specify the two colors to use (pal = is short for “palette” and is supplied with a vector of two color names. See the objects page for how the function c() makes a vector)

NOTE: For arguments specified with an equals symbol (e.g. coltotals = ...), their order among the arguments is not important (must still be within the parentheses and separated by commas).

age_pyramid(linelist, "age_group", "gender", proportional = TRUE, pal = c("orange", "purple"))

Packages

Packages contain functions.

On installation, R contains “base” functions that perform common elementary tasks. But many R users create specialized functions, which are verified by the R community and which you can download as a package for your own use.

One of the more challenging aspects of R is that there are often many functions or packages to choose from to complete a given task.

Functions are contained within packages which can be downloaded (“installed”) to your computer from the internet. Once a package is downloaded, you access its functions by loading the package with the library() command at the beginning of each R session.

# this loads the package "tidyverse" for use in the current R session
library(tidyverse)

NOTE: While you only have to install a package once, you must load it at the beginning of every R session using library() command, or an alternative like pacman’s p_load() function.

Think of R as your personal library: When you download a package your library gains a book of functions, but each time you want to use a function in that book, you must borrow that book from your library.

For clarity in this handbook, functions are usually preceeded by the name of their package using the :: symbol in the following way:

package_name::function_name()

Once a package is loaded for a session, this explicit style is not necessary. One can just use function_name(). However giving the package name is useful when a function name is common and may exist in multiple packages (e.g. plot()).
Using the package name will also load the package if it is not already loaded.

# This command uses the package "rio" and its function "import()" to import a dataset
linelist <- rio::import("linelist.xlsx", which = "Sheet1")

Dependencies
Packages often depend on other packages, and these are called “dependencies”. When a package is installed from CRAN, it will typically also install its dependenices.

Function Help

To read more about a function, you can try searching online for resources OR search in the Help tab of the lower-right RStudio pane.

Piping

Piping (%>%)

Two general approaches to R coding are:

1. Tidyverse - piping an object from function to function
   
2. defining intermediate objects (and older method, still worth knowing about)

Piping

Simply explained, the pipe operator (%>%) passes an intermediate output from one function to the next.
You can think of it as saying “then”. Many functions can be linked together with %>%.

• Piping emphasizes a sequence of actions, not the object the actions are being performed on
  

• Best when a sequence of actions must be performed on one object

• from magrittr. Included in dplyr and tidyverse

• Makes code more clean and easier to read, intuitive

• express a sequence of operations
  

• the object is altered and then passed on to the next function

Example:

# A fake example of how to bake a care using piping syntax

cake <- flour %>%       # to define cake, start with flour, and then...
  left_join(eggs) %>%   # add eggs
  left_join(oil) %>%    # add oil
  left_join(water) %>%  # add water
  mix_together(utensil = spoon, minutes = 2) %>%                # mix together
  bake(degrees = 350, system = "fahrenheit", minutes = 35) %>%  # bake
  let_cool()            # let it cool down

https://cfss.uchicago.edu/notes/pipes/#:~:text=Pipes%20are%20an%20extremely%20useful,code%20and%20combine%20multiple%20operations.

Piping is not a base function. To use piping, the dplyr package must be installed and loaded. Near the top of every template script is a code chunk that installs and loads the necessary packages, including dplyr. You can read more about piping in the documentation.

CAUTION: Remember that even when using piping to link functions, if the assignment operator (<-) is present, the object to the left will still be over-written (re-defined) by the right side.

TODO %<>% shortcut for re-defining the object and piping

Intermediate objects

Better if:
* You need to manipulate multiple objects
* There are intermediate steps that are meaningful and deserve separate object names

as changes are made - still handy to know

Risks: creating new objects for each step - lots of objects. If you use the wrong one you might not know. naming can be confusing, errors not easily detectable

either name each intermediate object, or overwrite the original, or combine all the functions together. all come with risks

https://style.tidyverse.org/pipes.html

# a fake example of how to bake a cake using this method (defining intermediate objects)
batter_1 <- left_join(flour, eggs)
batter_2 <- left_join(batter_1, oil)
batter_3 <- left_join(batter_2, water)

batter_4 <- mix_together(object = batter_3, utensil = spoon, minutes = 2)

cake <- bake(batter_4, degrees = 350, system = "fahrenheit", minutes = 35)

cake <- let_cool(cake)

Combine all functions together - also difficult to read

# an example of combining/nesting mutliple functions together - difficult to read
cake <- let_cool(bake(mix_together(batter_3, utensil = spoon, minutes = 2), degrees = 350, system = "fahrenheit", minutes = 35))

Key operators and functions

Key operators and functions

This section details operators in R, such as:
* Relational operators (less than, equal too..)
* Logical operators (and, or…)
* Handling missing values
* Mathematical operators and functions (+/-, >, sum(), median(), …)
* The %in% operator

Relational and logical operators

Relational operators compare values and are often used when defining new variables and subsets of datasets. Here are the common relational operators in R:

Function	Operator	Example	Example Result
Equal to	==	"A" == "a"	FALSE (because R is case sensitive) Note that == (double equals) is different from = (single equals), which acts like the assignment operator <-
Not equal to	!=	2 != 0	TRUE
Greater than	>	4 > 2	TRUE
Less than	<	4 < 2	FALSE
Greater than or equal to	>=	6 >= 4	TRUE
Less than or equal to	<=	6 <= 4	FALSE
Value is missing	is.na()	is.na(7)	FALSE (see section on missing values)
Value is not missing	!is.na()	!is.na(7)	TRUE

Logical operators, such as AND and OR, are often used to connect relational operators and create more complicated criteria. Complex statements might require parentheses ( ) for grouping and order of application.

Function	Operator
AND	&
OR	| (vertical bar)
Parentheses	( ) Used to group criteria together and clarify order

For example, below, we have a linelist with two variables we want to use to create our case definition, hep_e_rdt, a test result and other_cases_in_hh, which will tell us if there are other cases in the household. The command below uses the function case_when() to create the new variable case_def such that:

linelist_cleaned <- linelist_cleaned %>%
  mutate(case_def = case_when(
    is.na(hep_e_rdt) & is.na(other_cases_in_hh)           ~ NA_character_,
    hep_e_rdt == "Positive"                               ~ "Confirmed",
    hep_e_rdt != "Positive" & other_cases_in_hh == "Yes"  ~ "Probable",
    TRUE                                                  ~ "Suspected"
  ))

Criteria in example above	Resulting value in new variable “case_def”
If the value for variables hep_e_rdt and other_cases_in_hh are missing	NA (missing)
If the value in hep_e_rdt is “Positive”	“Confirmed”
If the value in hep_e_rdt is NOT “Positive” AND the value in other_cases_in_hh is “Yes”	“Probable”
If one of the above criteria are not met	“Suspected”

{{% notice tip %}} Note that R is case-sensitive, so “Positive” is different than “positive”… {{% /notice %}}

Missing Values

In R, missing values are represented by the special value NA (capital letters N and A - not in quotation marks). If you import data that records missing data in another way (e.g. 99, “Missing”, or .), you may want to re-code those values to NA.

To test whether a value is NA, use the special function is.na(), which returns TRUE or FALSE.

rdt_result <- c("Positive", "Suspected", "Positive", NA)   # two positive cases, one suspected, and one unknown
is.na(rdt_result)  # Tests whether the value of rdt_result is NA
## [1] FALSE FALSE FALSE  TRUE

To DO: SECTION ON OTHER NA TYPES: NA_character, NA_real etc. SECTION ON NULL

Mathematics and statistics

All the operators and functions in this page is automatically available using base R.

Mathematical operators

These are often used to perform addition, division, to create new columns, etc. Below are common mathematical operators in R. Whether you put spaces around the operators is not important.

Objective	Example in R
addition	2 + 3
subtraction	2 - 3
multiplication	2 * 3
division	30 / 5
exponent	2^3
order of operations	( )

Mathematical functions

Objective	Function
rounding	round(x, digits = n)
ceiling (round up)	ceiling(x)
floor (round down)	floor(x)
absolute value	abs(x)
square root	sqrt(x)
exponent	exponent(x)
natural logarithm	log(x)

Statistical functions:

CAUTION: The functions below will by default include missing values in calculations. Missing values will result in an output of NA, unless the argument na.rm=TRUE is specified

Objective	Function
mean (average)	mean(x, na.rm=T)
median	median(x, na.rm=T)
standard deviation	sd(x, na.rm=T)
quantiles*	quantile(x, probs)
sum	sum(x, na.rm=T)
minimum value	min(x, na.rm=T)
maximum value	max(x, na.rm=T)
range of numeric values	range(x, na.rm=T)
summmary**	summary(x)

*quantile(): x is the numeric vector to examine, and probs is a numeric vector with probabilities within 0 and 1.0, e.g c(0.5, 0.8, 0.85)

**summary(): gives a summary on a numeric vector including mean, median, and common percentiles

Other useful functions:

Objective	Function	Example
create a sequence	seq(from, to, by)	seq(1, 10, 2)
repeat x, n times	rep(x, ntimes)	rep(1:3, 2) or rep(c("a", "b", "c"), 3)
subdivide a numeric vector	cut(x, n)	cut(linelist$age, 5)

%in%

Loading Packages

Loading Packages

This section describes the several ways to install a package:
* Via the online package repository (CRAN)
* From a ZIP file
* From Github

CRAN

ZIP files

Github

Errors & Warnings

Errors & Warnings

This section explains:
* General syntax for writing R code
* Code assists
* the difference between errors and warnings

Common errors and warnings and their solutions can be found in X section (TODO).

General Syntax

A few things to remember when writing commands in R, to avoid errors and warnings:

• Always close parentheses - tip: count the number of opening “(” and closing parentheses “)” for each code chunk
• Avoid spaces in column and object names. Use underscore ( _ ) or periods ( . ) instead
• Keep track of and remember to separate a function’s arguments with commas
• R is case-sensitive, meaning Variable_A is different from variable_A

Code assists

Any script (RMarkdown or otherwise) will give clues when you have made a mistake. For example, if you forgot to write a comma where it is needed, or to close a parentheses, RStudio will raise a flag on that line, on the right side of the script, to warn you.

(/images/Warnings_and_Errors.png)

Errors and Warnings

When a command is run, the R Console may show you warning or error messages in red text.

• A warning means that R has completed your command, but had to take additional steps or produced unusual output that you should be aware of.

• An error means that R was not able to complete your command.

Look for clues:

• The error/warning message will often include a line number for the problem.

• If an object “is unknown” or “not found”, perhaps you spelled it incorrectly, forgot to call a package with library(), or forgot to re-run your script after making changes.

If all else fails, copy the error message into Google along with some key terms - chances are that someone else has worked through this already!

Recommended training

Recommended training

Cheatsheets

Courses

Importing data

Overview

Introduction to importing data

Packages

The key package we recommend for importing data is: rio. rio offers the useful function import() which can import many types of files into R.

The alternative to using rio would be to use functions from several other packages that are specific to a type of file (e.g. read.csv(), read.xlsx(), etc.). While these alternatives can be difficult to remember, always using rio::import() is relatively easy.

Optionally, the package here can be used in conjunction with rio. It locates files on your computer via relative pathways, usually within the context of an R project. Relative pathways are relative from a designated folder location, so that pathways listed in R code will not break when the script is run on a different computer.

This code chunk shows the loading of packages for importing data.

# Checks if package is installed, installs if necessary, and loads package for current session
pacman::p_load(rio, here)

import()

When you import a dataset, you are doing the following:

1. Creating a new, named data frame object in your R environment
   
2. Defining the new object as the imported dataset

The function import() from the package rio makes it easy to import many types of data files.

# An example:
#############
library(rio)                                                     # ensure package rio is loaded for use

# New object is defined as the imported data
my_csv_data <- import("linelist.csv")                            # importing a csv file

my_Excel_data <- import("observations.xlsx", which = "February") # import an Excel file

import() uses the file’s extension (e.g. .xlsx, .csv, .dta, etc.) to appropriately import the file. Any optional arguments specific to the filetype can be supplied as well.

You can read more about the rio package in this online vignette

CAUTION: In the example above, the datasets are assumed to be located in the working directory, or the same folder as the script.

Import from filepath

A filepath can be provided in full (as below) or as a relative filepath (see next tab). Providing a full filepath can be fast and may be the best if referencing files from a shared/network drive).

The function import() (from the package rio) accepts a filepath in quotes. A few things to note:

• Slashes must be forward slashes, as in the code shown. This is NOT the default for Windows filepaths.
  
• Filepaths that begin with double slashes (e.g. “//…”) will likely not be recognized by R and will produce an error. Consider moving these files to a “named” or “lettered” drive that begins with a letter (e.g. “J:” or “C:”). See the section on using Network Drive for more details on this issue.

# A demonstration showing how to import a specific Excel sheet
my_data <- rio::import("C:/Users/Neale/Documents/my_excel_file.xlsx")

Import Excel sheet

If importing a specific sheet from an Excel file, include the sheet name in the which = argument of import(). For example:

# A demonstration showing how to import a specific Excel sheet
my_data <- rio::import("my_excel_file.xlsx", which = "Sheetname")

If using the here() method to provide a relative pathway to import(), you can still indicate a specific sheet by adding the which = argument after the closing parenthese of the here() function.

# Demonstration: importing a specific Excel sheet when using relative pathways with the 'here' package
linelist_raw <- import(here("data", "linelists", "linelist.xlsx"), which = "Sheet1")`  

Select file manually

You can import data manually via one of these methods:

• Environment RStudio Pane, click “Import Dataset”, and select the type of data
• Click File / Import Dataset / (select the type of data)
  
• To hard-code manual selection, use the base R command file.choose() (leaving the parentheses empty) to trigger appearance of a pop-up window that allows the user to manually select the file from their computer. For example:

# A demonstration showing manual selection of a file. When this command is run, a POP-UP window should appear. 
# The filepath of the selected file will be supplied to the import() command.

my_data <- rio::import(file.choose())

TIP: The pop-up window may appear BEHIND your RStudio window.

Relative filepaths (here())

Relative filepaths differ from static filepaths in that they are relative from a R project root directory. For example:

• A static filepath: import("C:/Users/nsbatra/My Documents/R files/epiproject/data/linelists/ebola_linelist.xlsx")
  • Specific fixed path
  • Useful if multiple users are running a script hosted on a network drive
• A relative filepath: import(here("data", "linelists", "ebola_linelist.xlsx"))
  • Path is given in relation to a root directory (typically the root folder of an R project)
    
  • Best if working within an R project, or planning to zip and share entire project with others

The package here and it’s function here() facilitate relative pathways.

here() works best within R projects. When the here package is first loaded (library(here)), it automatically considers the top-level folder of your R project as “here” - a benchmark for all other files in the project.

Thus, in your script, if you want to import or reference a file saved in your R project’s folders, you use the function here() to tell R where the file is in relation to that benchmark.

If you are unsure where “here” is set to, run the function here() with the empty brackets:

# This command tells you the folder path that "here" is set to 
here::here()

Below is an example of importing the file “fluH7N9_China_2013.csv” which is located in the benchmark “here” folder. All you have to do is provide the name of the file in quotes (with the appropriate ending).

linelist <- import(here("fluH7N9_China_2013.csv"))

If the file is within a subfolder - let’s say a “data” folder - write these folder names in quotes, separated by commas, as below:

linelist <- import(here("data", "fluH7N9_China_2013.csv"))

Using the here() command produces a character filepath, which can then processed by the import() function.

# the filepath
here("data", "fluH7N9_China_2013.csv")

# the filepath is given to the import() function
linelist <- import(here("data", "fluH7N9_China_2013.csv"))

NOTE: You can still import a specific sheet of an excel file as noted in the Excel tab. The here() command only supplies the filepath.

Manual data entry

Entry by columns

Since a data frame is a combination of vertical vectors (columns), R by default expects manual entry of data to also be in vertical vectors (columns).

# define each vector (vertical column) separately, each with its own name
PatientID <- c(235, 452, 778, 111)
Treatment <- c("Yes", "No", "Yes", "Yes")
Death     <- c(1, 0, 1, 0)

CAUTION: All vectors must be the same length (same number of values).

The vectors can then be bound together using the function data.frame():

# combine the columns into a data frame, by referencing the vector names
manual_entry_cols <- data.frame(PatientID, Treatment, Death)

And now we display the new dataset:

# display the new dataset
DT::datatable(manual_entry_cols)

Entry by rows

Use the tribble function from the tibble package from the tidverse (onlinetibble reference).

Note how column headers start with a tilde (~). Also note that each column must contain only one class of data (character, numeric, etc.).
You can use tabs, spacing, and new rows to make the data entry more intuitive and readable. For example:

# create the dataset manually by row
manual_entry_rows <- tibble::tribble(
                        ~colA, ~colB,
                        "a",   1,
                        "b",   2,
                        "c",   3
                      )

And now we display the new dataset:

# display the new dataset
DT::datatable(manual_entry_rows)

Pasting from clipboard

If you copy data from elsewhere and have it on your clipboard, you can try the following command to convert those data into an R data frame:

manual_entry_clipboard <- read.table(file = "clipboard",
                                     sep = "t",           # separator could be tab, or commas, etc.
                                     header=TRUE)         # if there is a header row

Working with Dates

Overview

• It is important to make R recognize when a variable contains dates.
  
• Dates are an object class and can be tricky to work with.
  
• Here we present several ways to convert date variables to Date class.

Packages

The following packages are recommended for working with dates:

# Checks if package is installed, installs if necessary, and loads package for current session

pacman::p_load(aweek,      # flexibly converts dates to weeks, and vis-versa
               lubridate,  # for conversions to months, years, etc.
               linelist,   # function to guess messy dates
               ISOweek)    # another option for creating weeks

as.Date()

The standard, base R function to convert an object or variable to class Date is as.Date() (note capitalization).

as.Date() requires that the user specify the existing* format of the date*, so it can understand, convert, and store each element (day, month, year, etc.) correctly. Read more online about as.Date().

If used on a variable, as.Date() therefore requires that all the character date values be in the same format before converting. If your data are messy, try cleaning them or consider using guess_dates() from the linelist package.

It can be easiest to first convert the variable to character class, and then convert to date class:

1. Turn the variable into character values using the function as.character()

linelist_cleaned$date_of_onset <- as.character(linelist_cleaned$date_of_onset)

2. Convert the variable from character values into date values, using the function as.Date()
   (note the capital “D”)
   

• Within the as.Date() function, you must use the format= argument to tell R the current format of the date components - which characters refer to the month, the day, and the year, and how they are separated. If your values are already in one of R’s standard date formats (YYYY-MM-DD or YYYY/MM/DD) the format= argument is not necessary.

  • The codes are:
    %d = Day # (of the month e.g. 16, 17, 18…)
    %a = abbreviated weekday (Mon, Tues, Wed, etc.)
    %A = full weekday (Monday, Tuesday, etc.)
    %m = # of month (e.g. 01, 02, 03, 04)
    %b = abbreviated month (Jan, Feb, etc.)
    %B = Full Month (January, February, etc.)
    %y = 2-digit year (e.g. 89)
    %Y = 4-digit year (e.g. 1989)

For example, if your character dates are in the format DD/MM/YYYY, like “24/04/1968”, then your command to turn the values into dates will be as below. Putting the format in quotation marks is necessary.

linelist_cleaned$date_of_onset <- as.Date(linelist_cleaned$date_of_onset, format = "%d/%m/%Y")

TIP: The format= argument is not telling R the format you want the dates to be, but rather how to identify the date parts as they are before you run the command.

TIP:Be sure that in the format= argument you use the date-part separator (e.g. /, -, or space) that is present in your dates.

The as.character() and as.Date() commands can optionally be combined as:

linelist_cleaned$date_of_onset <- as.Date(as.character(linelist_cleaned$date_of_onset), format = "%d/%m/%Y")

If using piping and the tidyverse, the above command might look like this:

linelist_cleaned <- linelist_cleaned %>%
  mutate(date_of_onset = as.character(date_of_onset),
         date_of_onset = as.Date(date_of_onset, format = "%d/%m/%Y"))

Once complete, you can run a command to verify the class of the variable

# Check the class of the variable
class(linelist_cleaned$date_of_onset)  

Once the values are in class Date, R will by default display them in the standard format, which is YYYY-MM-DD.

lubridate

This is a section on using lubridate (Henry)

guess_dates()

The function guess_dates() attempts to read a “messy” date variable containing dates in many different formats and convert the dates to a standard format. You can read more online about guess_dates(), which is in the linelist package.

For example: guess_dates would see the following dates “03 Jan 2018”, “07/03/1982”, and “08/20/85” and convert them in the class Date to: 2018-01-03, 1982-03-07, and 1985-08-20.

linelist::guess_dates(c("03 Jan 2018", "07/03/1982", "08/20/85")) # guess_dates() not yet available on CRAN for R 4.0.2
                                                                  # try install via devtools::install_github("reconhub/linelist")

Some optional arguments for guess_dates() that you might include are:

• error_tolerance - The proportion of entries which cannot be identified as dates to be tolerated (defaults to 0.1 or 10%)
• last_date - the last valid date (defaults to current date)
  
• first_date - the first valid date. Defaults to fifty years before the last_date.

# An example using guess_dates on the variable dtdeath
data_cleaned <- data %>% 
  mutate(dtdeath = linelist::guess_dates(dtdeath, error_tolerance = 0.1, first_date = "2016-01-01")

Excel Dates

Excel stores dates as the number of days since December 30, 1899. If the dataset you imported from Excel shows dates as numbers or characters like “41369”… use the as.Date() function to convert, but instead of supplying a format as above, supply an origin date.

NOTE: You should provide the origin date in R’s default date format ("YYYY-MM-DD").

# An example of providing the Excel 'origin date' when converting Excel number dates
data_cleaned <- data %>% 
  mutate(date_of_onset = as.Date(date_of_onset, origin = "1899-12-30"))

How dates are displayed

Once dates are the correct class, you often want them to display differently (e.g. in a plot, graph, or table). For example, to display as “Monday 05 Jan” instead of 2018-01-05. You can do this with the function format(), which works in a similar way as as.Date(). Read more in this online tutorial

%d = Day # (of the month e.g. 16, 17, 18…) %a = abbreviated weekday (Mon, Tues, Wed, etc.)
%A = full weekday (Monday, Tuesday, etc.)
%m = # of month (e.g. 01, 02, 03, 04)
%b = abbreviated month (Jan, Feb, etc.)
%B = Full Month (January, February, etc.)
%y = 2-digit year (e.g. 89)
%Y = 4-digit year (e.g. 1989)
%h = hours (24-hr clock)
%m = minutes
%s = seconds %z = offset from GMT
%Z = Time zone (character)

An example of formatting today’s date:

# today's date, with formatting
format(Sys.Date(), format="%d %B %Y")
## [1] "07 December 2020"

# easy way to get full date and time (no formatting)
date()
## [1] "Mon Dec 07 16:00:07 2020"

# formatted date, time, and time zone (using paste0() function)
paste0(format(Sys.Date(), format= "%A, %b %d '%y, %z  %Z, "), format(Sys.time(), format = "%H:%M:%S"))
## [1] "Monday, Dec 07 '20, +0000  UTC, 16:00:07"

Calculating distance between dates

The difference between dates can be calculated by:

1. Correctly formating both date variable as class date (see instructions above)
   
2. Creating a new variable that is defined as one date variable subtracted from the other
3. Converting the result to numeric class (default is class “datediff”). This ensures that subsequent mathematical calculations can be performed.

Converting dates/time zones

TODO

Epidemiological weeks

The templates use the very flexible package aweek to set epidemiological weeks. You can read more about it on the RECON website

Dates in Epicurves

See the section on epicurves.

Dates miscellaneous

• Sys.Date( ) returns the current date of your computer
  
• Sys.Time() returns the current time of your computer
• date() returns the current date and time.

Cleaning data

Overview

This page demonstrates common steps necessary to clean a dataset. It uses a simulated Ebola case linelist, which is used throughout the handbook.

HOW TO READ: To emphasize the tidyverse coding approach, each cleaning step is explained individually and then incorporated into a “cleaning pipeline” - a series of cleaning actions linked together sequentially through pipes (LINK TO PIPES). The pipe begins with the “raw” data (linelist_raw) and ends with a “clean” dataset (linelist).

The cleaning steps demonstrated include:

• Loading the data
  
• column name cleaning
  
• column selection
  
• Designating column classes
  
• Filtering rows
  
• Re-coding values
  
• Creating groups (case_when())
• Dealing with character case (upper, lower, title, etc.)
• Factor columns

replace missing with dealing with cases (all lower, etc) case_when() factors

Preparation

Load packages

pacman::p_load(tidyverse,  # data manipulation and visualization
               janitor,    # data cleaning
               epitrix,    # data cleaning
               )

Load data

Import the raw dataset using the import() function from the package rio. (LINK HERE TO IMPORT PAGE)

linelist_raw <- import("ebola_simulated.xlsx")

You can view the original raw dataset below:

# display the linelist data as a table
DT::datatable(linelist_raw, rownames = FALSE, filter="top", options = list(pageLength = 5, scrollX=T) )

A cleaning pipeline

As explained in the section on dplyr and tidyverse coding style (LINK HERE), a chain of ‘verb’ functions operate on a dataset through ‘pipes’ (%>%), passing the output from one verb to the next.

order is important

TO DO

Column names

column names are used so often, it is best that they have “clean” syntax. We suggest the following:

• short
• no spaces (replaced with underscores (_),
• no unusual characters (&, #…)
  
• similar nomenclature (e.g. all dates like date_onset, date_report, date_death)

The names of linelist_raw are below. We can see that there are some with spaces. We also have different naming patterns for dates (‘date onset’ and ‘infection date’).

names(linelist_raw)
##  [1] "row_num"         "case_id"         "generation"      "infection date" 
##  [5] "date onset"      "hosp date"       "date_of_outcome" "outcome"        
##  [9] "gender"          "hospital"        "lon"             "lat"            
## [13] "infector"        "source"          "age"             "age_unit"       
## [17] "fever"           "chills"          "cough"           "aches"          
## [21] "vomit"

Note: To use a column names that include spaces, surround the name with back-ticks, for example: linelist$`infection date` 
On a keyboard, the back-tick (`) is different from the single quotation mark ('), and is sometimes on the same key as the tilde (~).

Automatic syntax cleaning

The function clean_names() from the package janitor standardizes column names by transliterating to unique ASCII names by doing the following:

• Converts all names to consist of only underscores, numbers, and letters
  
• Accented characters are transliterated to ASCII (e.g. german o with umlaut becomes “o”, spanish “enye” becomes “n”)
  
• Capitalization preference can be specified using the case = argument (“snake” is default, alternatives include “sentence”, “title”, “small_camel”…)
  
• You can designate specific replacements with the replace = argument (e.g. replace = c(onset = “date_of_onset”))
  
• Here is an online vignette

# send the dataset through the function clean_names()
linelist <- linelist_raw %>% 
  janitor::clean_names()

# see the new names
names(linelist)
##  [1] "row_num"         "case_id"         "generation"      "infection_date" 
##  [5] "date_onset"      "hosp_date"       "date_of_outcome" "outcome"        
##  [9] "gender"          "hospital"        "lon"             "lat"            
## [13] "infector"        "source"          "age"             "age_unit"       
## [17] "fever"           "chills"          "cough"           "aches"          
## [21] "vomit"

Manual column name cleaning

Re-naming columns manually is often necessary. Below, re-naming is performed using the rename() function from the dplyr package, as part of a pipe chain. rename() uses the style “NEW = OLD”, the new column name is given before the old column name.

# CLEANING 'PIPE' CHAIN (starts with raw data and pipes it through cleaning steps)
##################################################################################
linelist <- linelist_raw %>%
    
    # standardize column name syntax
    janitor::clean_names() %>% 
    
    # manually re-name columns
           # NEW name             # OLD name
    rename(date_infection       = infection_date,
           date_hospitalisation = hosp_date,
           date_outcome         = date_of_outcome)

Now you can see that the columns names have been changed:

# display the linelist data as a table
DT::datatable(linelist, rownames = FALSE, filter="top", options = list(pageLength = 5, scrollX=T) )

Select columns

CAUTION: This tab may follow from previous tabs.

Often the first step of cleaning data is selecting the columns you want to work with, and to set their order in the dataframe. In a dplyr chain of verbs, this is done with select(). Note that in these examples we modify linelist with select(), but do not assign/overwrite. We just display the resulting new column names, for purpose of example.

Here are all the column names in the linelist:

names(linelist)
##  [1] "row_num"              "case_id"              "generation"          
##  [4] "date_infection"       "date_onset"           "date_hospitalisation"
##  [7] "date_outcome"         "outcome"              "gender"              
## [10] "hospital"             "lon"                  "lat"                 
## [13] "infector"             "source"               "age"                 
## [16] "age_unit"             "fever"                "chills"              
## [19] "cough"                "aches"                "vomit"

With select() you can do the following:

Select only the columns you want to remain, and their order of appearance

# linelist dataset is piped through select() command, and names() prints just the column names
linelist %>% 
  select(case_id, date_onset, date_hospitalisation, fever) %>% 
  names() # display the column names
## [1] "case_id"              "date_onset"           "date_hospitalisation"
## [4] "fever"

Indicate which columns to remove by placing a minus symbol “-” in front of the column name (e.g. select(-outcome)), or a vector of column names (as below). All other columns will be retained.
Inside select() you can use normal operators such as c() to list several columns, : for consecutive columns, ! for opposite, & for AND, and | for OR.

linelist %>% 
  select(-c(date_onset, fever:vomit)) %>% # remove onset and all symptom columns
  names()
##  [1] "row_num"              "case_id"              "generation"          
##  [4] "date_infection"       "date_hospitalisation" "date_outcome"        
##  [7] "outcome"              "gender"               "hospital"            
## [10] "lon"                  "lat"                  "infector"            
## [13] "source"               "age"                  "age_unit"

Re-order the columns - use everything() to signify all other columns not specified in the select() command:

# move case_id, date_onset, date_hospitalisation, and gender to beginning
linelist %>% 
  select(case_id, date_onset, date_hospitalisation, gender, everything()) %>% 
  names()
##  [1] "case_id"              "date_onset"           "date_hospitalisation"
##  [4] "gender"               "row_num"              "generation"          
##  [7] "date_infection"       "date_outcome"         "outcome"             
## [10] "hospital"             "lon"                  "lat"                 
## [13] "infector"             "source"               "age"                 
## [16] "age_unit"             "fever"                "chills"              
## [19] "cough"                "aches"                "vomit"

As well as everything() there are several special functions that work within select(), namely:

• everything() - all other columns not mentioned
  
• last_col() - the last column
  
• starts_with() - matches to a specified prefix. Example: select(starts_with("date"))
• ends_with() - matches to a specified suffix. Example: select(ends_with("_end"))
  
• contains() - columns containing a character string. Example: select(contains("time"))
• matches() - to apply a regular expression (regex). Example: select(contains("[pt]al"))
  
• num_range() -
• any_of() - matches if column is named. Useful if name might not exist. Example: select(any_of(date_onset, date_death, cardiac_arrest))
  
• where() - applies a function to all columns and selects those which are TRUE

Adding select()to the cleaning pipe chain:

In the linelist, there is one column we do not need: row_num. Remove it by adding a select() command to the cleaning pipe chain:

# CLEANING 'PIPE' CHAIN (starts with raw data and pipes it through cleaning steps)
##################################################################################
linelist <- linelist_raw %>%
    
    # standardize column name syntax
    janitor::clean_names() %>% 
    
    # manually re-name columns
           # NEW name             # OLD name
    rename(date_infection       = infection_date,
           date_hospitalisation = hosp_date,
           date_outcome         = date_of_outcome) %>% 
    
    # remove column
    select(-row_num)

select() as a stand-alone command

Select can also be used as an independent command (not in a pipe chain). In this case, the first argument is the original dataframe to be operated upon.

# Create a new linelist with only three columns
linelist_age <- select(linelist, case_id, age, age_unit)

Fix classes

CAUTION: This tab may follow from previous tabs.

See section on object classes

Here we want to ensure that the class of each column is appropriate. First we run some checks on the classes of important columns.

The class of the “age” column is character. To perform analysis, we need those numbers to be recognized as numeric!

class(linelist$age)
## [1] "character"

The class of the “date_onset” column is also character! To perform analysis, these dates must be recognized as dates!

class(linelist$date_onset)
## [1] "character"

Use table() or another method to see all the values, can see that we see that one date was entered in a different format (15 April 2014) than all the others!

## 
## 15 April 2014    2010-05-07    2010-05-08    2010-05-27    2010-06-15 
##             1             1             1             1             1 
##    2010-06-18 
##             1

This means before we can classify “date_onset” as a date, this value must be fixed to be the same format as the others. You can fix the date in the source data. Or, we can do this using mutate() and recode() in our cleaning pipe chain, before the commands to convert to class Date. LINK TO CLASSIFYING column AS DATE.

The new mutate line can be read as: mutate date_onset to equal date_onset recoded so that OLD VALUE is changed to NEW VALUE. Note that this pattern (OLD = NEW) is the opposite of most R patterns. The R development community is working on revising this.

# CLEANING 'PIPE' CHAIN (starts with raw data and pipes it through cleaning steps)
##################################################################################
linelist <- linelist_raw %>%
    
    # standardize column name syntax
    janitor::clean_names() %>% 
    
    # manually re-name columns
           # NEW name             # OLD name
    rename(date_infection       = infection_date,
           date_hospitalisation = hosp_date,
           date_outcome         = date_of_outcome) %>% 
  
    # remove column
    select(-row_num) %>% 

# ABOVE ARE UPSTREAM CLEANING STEPS ALREADY DISCUSSED
  ###################################################
  # fix incorrect values                 # old value       # new value
  mutate(date_onset = recode(date_onset, "15 April 2014" = "2014-04-15")) %>% 
  
  # correct the class of the columns
  mutate(age           = as.numeric(age),
         date_onset    = as.Date(date_onset, format = "%Y-%m-%d"))

Especially after converting to class date, check your data visually or with table() to confirm that they were converted correctly! For as.Date(), the format = argument is often a source of errors.

Fix class for multiple columns at once

class(linelist$date_infection)
## [1] "POSIXct" "POSIXt"
head(linelist$date_infection)
## [1] "2014-04-09 UTC" NA               NA               "2014-05-07 UTC"
## [5] NA               "2014-05-06 UTC"

You can use The dplyr function across() with mutate() to convert several columns at once to a new class. across() allows you to specify which columns you want a function to apply to. Below, we want to mutate the columns where is.POSIXct() (a type of date/time class that shows unnecessary timestamps) is TRUE, and apply the function is.Date() to them, in order to convert them to class “date”.

• Note that within across() we also use the function where().
  
• Note that is.POSIXct is from the package lubridate. Other similar functions (is.character(), is.numeric(), and is.logical()) are from base R
  
• Note that the functions in across() are written without the empty parentheses ()

linelist <- linelist %>% 
  mutate(across(where(lubridate::is.POSIXct), as.Date))

Filter rows

CAUTION: This tab may follow from previous tabs.

After selecting columns, a typical cleaning step is to filter the dataframe for specific rows using the dplyr verb filter(). Within filter(), give the logic that must be TRUE for a row in the dataset to be kept. A simple example below filters to keep only the rows where case_id is not missing (three rows are removed):

linelist <- linelist %>% 
  filter(!is.na(case_id))  # keep only rows where case_id is not missing

A more complex example:

Run a simple histogram of onset dates to see that a second smaller outbreak from 2012-2013 is also included in this dataset. For our analyses, we want to remove entries from this earlier outbreak.

hist(linelist$date_onset, breaks = 50)

If we simply filter linelist by date of onset (after June 2013) we may make a mistake! Applying filter(date_onset > as.Date("2013-06-01"))) would accidentally remove any rows in the later epidemic with a missing date of onset!

DANGER: Filtering to greater than (>) or less than (<) a date can remove any rows with missing date values (NA)! This is because NA is treated as infinitely large and small.

We also know that this first epidemic occurred at Hospital A, Hospital B, and there were 10 cases at Connaught Hospital. Hospitals A & B did not have cases in the second epidemic, but Connaught Hospital had many. This is a complex filter to apply - it is wise to tabulate these columns to know exactly how many rows we expect should be removed.

Let’s examine a cross-tabulation to make sure we exclude only the correct rows:

table(Hospital  = linelist$hospital,                     # hospital name
      YearOnset = lubridate::year(linelist$date_onset),  # year of the date_onset
      useNA     = "always")                              # show missing values
##                                               YearOnset
## Hospital                                       2010 2011 2014 2015 <NA>
##   Connaught Hopital                               0    0   38    6    3
##   Connaught Hospital                              8    2 1322  327   66
##   Hospital A                                     40   13    0    0    1
##   Hospital B                                     41   12    0    0    1
##   Military Hopital                                0    0   20    8    2
##   Military Hospital                               0    0  580  168   38
##   Mitylira Hopital                                0    0    1    0    0
##   Mitylira Hospital                               0    0   59   17    3
##   other                                           0    0  679  168   38
##   Princess Christian Maternity Hopital (PCMH)     0    0   10    1    0
##   Princess Christian Maternity Hospital (PCMH)    0    0  306   90   15
##   Rokupa Hopital                                  0    0   10    1    0
##   Rokupa Hospital                                 0    0  332   94   17
##   <NA>                                            0    0 1106  296   67

We want to exclude only the nrow(linelist %>% filter(hospital %in% c("Hospital A", "Hospital B") | date_onset < as.Date("2013-06-01"))) rows from 2012 and 2013 at those three hospitals (A, B, and Connaught), including the 2 from Hospitals A & B with missing onset dates, but not any others with missing onset dates. We start with a linelist of nrow(linelist). Here is our statement:

linelist <- linelist %>% 
  filter(date_onset > as.Date("2013-06-01") | (is.na(date_onset) & !hospital %in% c("Hospital A", "Hospital B")))

nrow(linelist)
## [1] 5888

When we re-make the cross-tabulation, we see that Hospitals A & B are removed completely, the 10 Connaught Hospital cases from 2012 & 2013 are removed, and all other values are the same - just as we wanted.

table(Hospital  = linelist$hospital,                     # hospital name
      YearOnset = lubridate::year(linelist$date_onset),  # year of the date_onset
      useNA     = "always")                              # show missing values
##                                               YearOnset
## Hospital                                       2014 2015 <NA>
##   Connaught Hopital                              38    6    3
##   Connaught Hospital                           1322  327   66
##   Military Hopital                               20    8    2
##   Military Hospital                             580  168   38
##   Mitylira Hopital                                1    0    0
##   Mitylira Hospital                              59   17    3
##   other                                         679  168   38
##   Princess Christian Maternity Hopital (PCMH)    10    1    0
##   Princess Christian Maternity Hospital (PCMH)  306   90   15
##   Rokupa Hopital                                 10    1    0
##   Rokupa Hospital                               332   94   17
##   <NA>                                         1106  296   67

Multiple filter statements can be separated by commas, or you can always pipe to a separate filter() statement for clarity. Adding these filter statements to the cleaning pipe chain now looks like this:

# CLEANING 'PIPE' CHAIN (starts with raw data and pipes it through cleaning steps)
##################################################################################
linelist <- linelist_raw %>%
    
    # standardize column name syntax
    janitor::clean_names() %>% 
    
    # manually re-name columns
           # NEW name             # OLD name
    rename(date_infection       = infection_date,
           date_hospitalisation = hosp_date,
           date_outcome         = date_of_outcome) %>% 
  
    # remove column
    select(-row_num) %>% 

    # fix incorrect values                 # old value       # new value
    mutate(date_onset = recode(date_onset, "15 April 2014" = "2014-04-15")) %>% 
    
    # correct the class of the columns
    mutate(age           = as.numeric(age),
           date_onset    = as.Date(date_onset, format = "%Y-%m-%d")) %>% 
    
  # ABOVE ARE UPSTREAM CLEANING STEPS ALREADY DISCUSSED
    ###################################################
    filter(!is.na(case_id),  # keep only rows where case_id is not missing
           date_onset > as.Date("2013-06-01") | (is.na(date_onset) & !hospital %in% c("Hospital A", "Hospital B"))
           ) # close filter

Standalone command

filter(dataset, criteria) OR subset like: dataset_new <- dataset[criteria,criteria]

nrow(linelist %>% filter())

Creating and modifying columns

CAUTION: This tab may follow from previous tabs.

We advise creating new columns with dplyr functions as part of a chain of such verb functions (e.g. filter, mutate, etc.)
If in need of a stand-alone command, you can use plyr or also the base R style to create a new column.

Using mutate() (dplyr)

The verb mutate() is used to add a new column or modify an existing one. Below are some example of creating new columns with mutate(). The syntax is: new_column_name = value or function. It is best practice to separate each new column with a comma and new line.

linelist <- linelist %>%                   # creating new, or modifying old dataset
  mutate(new_var_dup    = case_id,                  # new column = duplicate/copy another column
         new_var_static = 7,                   # new column = all values the same
         new_var_static = new_var_static + 5,  # you can overwrite a column, and can modify a column multiple times
         new_var_calc   = (age / 12),          # new column = a calculation
         new_var_paste  = paste0(hospital, " (", date_hospitalisation, ")") # new column = pasting together values from other columns
         ) 

Scroll to the right to see the new columns:

# display the linelist data as a table
DT::datatable(linelist, rownames = FALSE, filter="top", options = list(pageLength = 5, scrollX=T) )

TIP: The verb transmute() adds new columns just like mutate() but also drops/removes all other columns that you do not mention.

Simple recoding of values

To recode the values in a column, mutate() is also used.

For example, in the linelist we need to clean the values in the column “hospital”. There are several incorrect spelling, and many missing values.

table(linelist$hospital, useNA = "always")
## 
##                            Connaught Hopital 
##                                           47 
##                           Connaught Hospital 
##                                         1715 
##                             Military Hopital 
##                                           30 
##                            Military Hospital 
##                                          786 
##                             Mitylira Hopital 
##                                            1 
##                            Mitylira Hospital 
##                                           79 
##                                        other 
##                                          885 
##  Princess Christian Maternity Hopital (PCMH) 
##                                           11 
## Princess Christian Maternity Hospital (PCMH) 
##                                          411 
##                               Rokupa Hopital 
##                                           11 
##                              Rokupa Hospital 
##                                          443 
##                                         <NA> 
##                                         1469

To change spellings one-by-one, you can use the recode() function within the mutate function. The code is saying that the column “hospital” should be defined as the current column “hospital” but with certain changes (syntax is OLD = NEW). Don’t forget commas!

linelist <- linelist %>% 
  mutate(hospital = recode(hospital,
                           # OLD = NEW
                           "Mitylira Hopital"  = "Military Hospital",
                           "Mitylira Hospital" = "Military Hospital",
                           "Military Hopital"  = "Military Hospital",
                           "Connaught Hopital" = "Connaught Hospital",
                           "Rokupa Hopital"    = "Rokupa Hospital",
                           "other"             = "Other",
                           "Princess Christian Maternity Hopital (PCMH)" = "Princess Christian Maternity Hospital (PCMH)"
                           ))

table(linelist$hospital, useNA = "always")
## 
##                           Connaught Hospital 
##                                         1762 
##                            Military Hospital 
##                                          896 
##                                        Other 
##                                          885 
## Princess Christian Maternity Hospital (PCMH) 
##                                          422 
##                              Rokupa Hospital 
##                                          454 
##                                         <NA> 
##                                         1469

TIP: The number of spaces before and after an equals sign does not matter. Make your code easier to read by aligning the = for all or most rows. Also, consider adding a hashed comment row to clarify for future readers which side is OLD and which side is NEW.

TIP: Sometimes a blank character value exists in a dataset (not recognized as R’s value for missing - NA). You can reference this value with two quotation marks with no space inbetween ("").

Special recoding functions

To change missing values to a character value, such as “Missing”, use the function replace_na() in the same manner as recode above:

linelist <- linelist %>% 
  mutate(hospital = replace_na(hospital, "Missing"))

Likewise you can quickly convert character values to NA using na_if(), as below:

linelist <- linelist %>% 
  mutate(hospital = na_if(hospital, "Missing"))

logical re-coding

Intro For simple cases you can use ifelse() or if_else(). In most cases it is better to use case_when().

ifelse() and if_else():
These commands are simplified versions of an if and else statement. The general syntax is ifelse(condition, value if TRUE, value if FALSE). if_else() is a special version from dplyr that handles dates.

Stringing together ifelse statements - NOT ADVISED!! Difficult to read and keep track of.

IMAGE of ifelse string with X across is.

Use case-when() instead.

You can reference other columns with the ifelse() function within mutate():

linelist <- linelist %>% 
  mutate(date_death = if_else(outcome == "Death", date_outcome, NA_real_))

linelist <- linelist %>% 
  mutate(date_death = if_else(outcome == "Death", date_outcome, NA_real_))

Missing if… na_if() lead(), lag() cumsum(), cummean(), cummin(), cummax(), cumany(), cumall(),

coalesce()

if_else(), ifelse()

recode CLEANING MISSPELLINGS HOSPITAL NAME

Replace

Using cleaning dictionaries

CAUTION: This tab may follow from previous tabs.

## load cleaning rules and only keep columns in mll
mll_cleaning_rules <- import(here("dictionaries/mll_cleaning_rules.xlsx")) %>%
  filter(column %in% c(names(mll_raw), ".global"))

## define columns that are not cleand
unchanged <- c(
  "epilink_relationship",
  "narratives",
  "epilink_relationship_detail"
)

mll_clean <- mll_raw %>%
  ## convert to tibble
  as_tibble() %>%
  ## clean columns using cleaning rules
  clean_data(
    wordlists = mll_cleaning_rules,
    protect = names(.) %in% unchanged
  )

Mutating grouped data

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Using mutate on GROUPED dataframes https://dplyr.tidyverse.org/reference/mutate.html

Taken from website above:

Because mutating expressions are computed within groups, they may yield different results on grouped tibbles. This will be the case as soon as an aggregating, lagging, or ranking function is involved. Compare this ungrouped mutate:

starwars %>%
  select(name, mass, species) %>%
  mutate(mass_norm = mass / mean(mass, na.rm = TRUE))
With the grouped equivalent:

starwars %>%
  select(name, mass, species) %>%
  group_by(species) %>%
  mutate(mass_norm = mass / mean(mass, na.rm = TRUE))
The former normalises mass by the global average whereas the latter normalises by the averages within species levels.

Using base R

If you need to write a stand-alone command using base R (e.g. not part of a chain of dplyr verbs), then you can create a new column by assigning it a value. In the command below, the column new_var does not exist until after the command is executed. In this simple example the column is assigned the static value “new value”, so for all rows the value will be “new value”.

linelist_raw$new_var <- "new value"

You can also give the new column a dyanmic value as shown below, or using the case_when() command explained in the next tab.

# mathematical operators on another column
linelist_raw$new_var <- linelist_raw$OLD_var * 4

Groups by condition (case_when())

Recoding with logic using case_when()

If you need to use logic statements to recode values, or want to use operators like %in%, use dplyr’s case_when() instead. If you use case_when() please read the thorough explanation HERE LINK, as there are important differences from recode() in syntax and logic order!

linelist <- linelist %>% 
  mutate(hospital = case_when(hospital == "Connaught Hopital"                           ~ "Connaught Hospital",
                              hospital == "Rokupa Hopital"                              ~ "Rokupa Hospital", 
                              hospital %in% c("Mitylira Hopital", 
                                             "Mitylira Hospital",
                                             "Mitylira Hospital",
                                             "Military Hopital")                        ~ "Military Hospital",
                              is.na(hospital)                                           ~ "Missing",
                              hospital == "Princess Christian Maternity Hopital (PCMH)" ~ "Princess Christian Maternity Hospital (PCMH)",
                              TRUE                                                      ~  hospital) 
)
table(linelist$hospital, useNA = "always")
## 
##                           Connaught Hospital 
##                                         1762 
##                            Military Hospital 
##                                          896 
##                                      Missing 
##                                         1469 
##                                        Other 
##                                          885 
## Princess Christian Maternity Hospital (PCMH) 
##                                          422 
##                              Rokupa Hospital 
##                                          454 
##                                         <NA> 
##                                            0

CAUTION: This tab may follow from previous tabs.

TODO tutorial on using case_when()

Numeric groups

For example, creating age groups cut()

case_when()

age_categories() (R4Epis package)

by percentile

WHAT TO DO IF AGE IS SPREAD ACROSS TWO VARAIBLES (e.g. numeric age + unit)

Highest in hierarchy

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Within a group, indicate/convert to the highest value in the group

Santa Clara County example - COVID contact tracing data - classification of multiple phone call records from same person into the highest category. (classify all as the highest of the group)

Changing multiple variables at one time

across dplyr

Missing Data

Overview

dealing with missing data percent missing over time etc.

Percent missing over time

Or change in percent of anything (X) over time, really.

lines <- linelist %>%
  mutate(date_of_onset = as.Date(date_of_onset, format = "%d/%m/%Y"),
         week = aweek::week2date(aweek::date2week(date_of_onset))) %>% 
  group_by(week) %>% 
  summarize(n_obs = n(),
            dt_hosp_missing = sum(date_of_hospitalisation == "" | is.na(date_of_hospitalisation)),
            dt_hosp_p_miss = dt_hosp_missing / n_obs,
            
            outcome_missing = sum(outcome == "" | is.na(outcome)),
            outcome_p_miss = outcome_missing / n_obs) %>%
  reshape2::melt(id.vars = c("week")) %>%
  filter(grepl("_p_", variable)) %>% 
  
  ggplot()+
    geom_line(aes(x = week, y = value, group = variable, color = variable), size = 1, stat = "identity")+
    labs(title = "Missingness in variables, as proportion of ",
         #subtitle = str_glue("As of {format(report_date, '%d %b')}"), 
         x = "Week",
         y = "Proportion missing",
         fill = "CalREDIE Variable") + 
    scale_color_discrete(name = "Variable", labels = c("Date of Hospitalization Missing", "Outcome Missing"))+
    scale_y_continuous(breaks = c(seq(0,1,0.1)))
    #theme_cowplot()#+
    #theme(legend.position = element_text("none"))

lines

Pivoting

Overview

(pivoting/melting etc.) Transforming datasets from wide-to-long, or long-to-wide…

Wide-to-long

Transforming a dataset from wide to long

Data

We start with data that is in a wide format, e.g. our linelist.

DT::datatable(linelist, rownames = FALSE, filter="top", options = list(pageLength = 5, scrollX=T) )

pivot_longer()

#tidyr::pivot_longer(linelist, dplyr::vars(-age, -date_of_hospitalisation), names_to = "variable", values_to = "value" )

Result

Long-to-wide

dplyr pivot_wider()

Data

Pivot wider

Result

Grouping/aggregating data

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Tidyverse - grouping by values

.drop=F in group_by() command

Preparation

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Data preparation steps such as:

• Loading dataset
  
• Adding or changing variables
  
• melting, pivoting, grouping, etc.

group_by()

aggregate()

Resources

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Deduplicating

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Overview

1. identifying and eliminating duplicate records
2. “Rolling up” or combining multiple records into one

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sub-tab 1

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sub-tab 2

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Option 1

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Matching & joining datasets

Overview

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Preparation

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sub-tab 1

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Joins

antijoins as well

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Probabalistic matching

rowmatcher other options (finlay?)

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Character manipulation/search (regex)

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Overview

1. Syntax/regex
2. Splitting string variables
3. Searcihg ICD codes chief complaints
4. classifing occupations (Santa Clara County example)

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sub-tab 1

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Tables

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Making nice tables

Overview

Many audiences and reasons for the tables…

Preparation

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• melting, pivoting, grouping, etc.

Manually

From data frame

knitr::kable DT

Summarizing dataframe

From modelresults

For publication

Other

quickly changing the denominator (per 100,000, etc.)

Resources

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Descriptive analysis

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linelist <- linelist %>% 
  mutate(age = as.numeric(age))

Summary Statistics

Summary of numeric variable

summary(linelist$age)
##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max.    NA's 
##    2.00   47.25   60.00   56.91   72.00   91.00       2

Mean

Note the argument na.rm=T, which removes missing values from the calculation.
If missing values are not excluded, the returned value will be NA (missing).

mean(linelist$age)
## [1] NA
mean(linelist$age, na.rm=TRUE)  # with the na.rm= argument set to TRUE
## [1] 56.91045

Median

Note the argument na.rm=T, which removes missing values from the calculation.
If missing values are not excluded, the returned value will be NA (missing).

median(linelist$age)
## [1] NA
median(linelist$age, na.rm=TRUE)  # with the na.rm= argument set to TRUE
## [1] 60

Range

Note the argument na.rm=T, which removes missing values from the calculation.
If missing values are not excluded, the returned value will be NA (missing).

range(linelist$age)
## [1] NA NA
range(linelist$age, na.rm=TRUE)  # with the na.rm= argument set to TRUE
## [1]  2 91

Standard Deviation

Note the argument na.rm=T, which removes missing values from the calculation.
If missing values are not excluded, the returned value will be NA (missing).

sd(linelist$age)
## [1] NA
sd(linelist$age, na.rm=TRUE)  # with the na.rm= argument set to TRUE
## [1] 20.43124

Percentile

Note the argument na.rm=T, which removes missing values from the calculation.
If missing values are not excluded, the returned value will be NA (missing).

stats::quantile(linelist$age, na.rm=TRUE)  # default %iles
##    0%   25%   50%   75%  100% 
##  2.00 47.25 60.00 72.00 91.00
stats::quantile(linelist$age, probs = c(.05, 0.5, 0.75, 0.98), na.rm=TRUE) # percentiles specified
##    5%   50%   75%   98% 
## 12.90 60.00 72.00 86.34

Frequency/cross-tabs

Frequency table of 1 and 2 categorical variables

table(linelist$province)
## 
##     Anhui   Beijing    Fujian Guangdong     Hebei     Henan     Hunan   Jiangsu 
##         4         3         5         1         1         4         2        28 
##   Jiangxi  Shandong  Shanghai    Taiwan  Zhejiang 
##         6         2        33         1        46

x <- table(linelist$province, linelist$gender)
#janitor::adorn_totals(x)

A table with 3 variables

table_3vars <- table(linelist$province, linelist$gender, linelist$outcome)

ftable(table_3vars)
##              Death Recover
##                           
## Anhui     f      1       0
##           m      1       1
## Beijing   f      0       1
##           m      0       1
## Fujian    f      0       0
##           m      0       3
## Guangdong f      0       0
##           m      0       0
## Hebei     f      1       0
##           m      0       0
## Henan     f      0       0
##           m      1       3
## Hunan     f      1       0
##           m      0       1
## Jiangsu   f      2       3
##           m      2       7
## Jiangxi   f      0       2
##           m      1       1
## Shandong  f      0       0
##           m      0       2
## Shanghai  f      3       3
##           m     12      10
## Taiwan    f      0       0
##           m      0       0
## Zhejiang  f      1       3
##           m      5       5

Summary by group

count_data %>% 
  group_by(District) %>% 
  summarise(n_obs = n(), # number of observations
            range_date = max(Date, na.rm=T) - min(Date, na.rm=T)
            )

Correlations

T-tests

Resources

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Simple statistical test

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quant/quant, quant/cat, cat/cat t-tests odds ratios, mantel-haensel, etc.

Preparation

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Data preparation steps such as:

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Sub-tabs if necessary. Re-name as needed.

Option 2

This tab can be re-named. This tab should demonstrate execution of the task a more standard/core package (e.g. ggplot2, or base R) that allows for more flexibility in the output or more package stability. For example, showing how to create an epicurve using ggplot2.

Option 2 sub-tab

Sub-tabs if necessary. Re-name as needed.

Resources

This tab should stay with the name “Resources”. Links to other online tutorials or resources.

Epidemic curves

The Page title should be succinct. Consider adding a tag with no spaces into the curly brackets, such as below. This can be used for internal links within the handbook. {#title_tag .tabset .tabset-fade}

Overview

Preparation

Keep the title of this section as “Preparation”.
Data preparation steps such as:

• Loading dataset
  
• Adding or changing variables
  
• melting, pivoting, grouping, etc.

Packages

This code chunk shows the loading of packages required for the analyses.

# Create vector of names of required packages:
packages_epicurve <- c("rio",          # File import
                       "here",         # File locator
                       "tidyverse",    # data manipulation
                       "ggplot2",      # Produce plots and graphs
                       "aweek",        # working with dates
                       "lubridate",    # Manipulate dates    
                       "incidence",    # an option for epicurves of linelist data
                       "stringr",      # Search and manipulate character strings
                       "forcats",      # working with factors
                       "RColorBrewer", # Color palettes from colorbrewer2.org
                       "DT"            # produce tables for this html handbook
                       )               ### close vector of packages

# Checks if package is installed, installs if necessary, and loads package for current session
pacman::p_load(packages_epicurve, character.only=TRUE)

Load data

Two example datasets are used in this section:

• Linelist of individual cases from a simulated Ebola epidemic
  
• Aggregated counts by hospital from the same simulated Ebola epidemic

If viewing in Google Chrome, you can access these datasets in Microsoft Excel by clicking HERE and HERE. TODO.

The dataset is imported using the import() function from the rio package. See the page on importing data for various ways to import data. The linelist and aggregated versions of the data are displayed below.

For most of this document, the linelist dataset will be used. The aggregated counts dataset will be used at the end.

# import the linelist into R
linelist <- rio::import("ebola_simulated.xlsx")

Do cleaning steps as necessary!!! To Do

Review data

Review the two datasets and notice the differences

Linelist dataset

# display the linelist data as a table
DT::datatable(linelist, rownames = FALSE, filter="top", options = list(pageLength = 5, scrollX=T) )

Aggregated counts dataset

# display the linelist data as a table
DT::datatable(count_data, rownames = FALSE, filter="top", options = list(pageLength = 5, scrollX=T) )

Set parameters

You may want to set certain parameters for production of a report, such as the date for which the data is current (the “data date”). In this case, we set this date as 27 July 2013.

Now we can reference the object data_date into the code and have it reference that date.

## set the report date for the report
## note: can be set to Sys.Date() for the current date
data_date <- as.Date("2013-07-27")

Clean dates

Optionally, it can be nice to identify all the date variables and store their names in a vector. This can be done by individually naming them, or by searching for them by looking for keywords.

Method 1

# Method 1. Define date variables explicitly in a vector
DateVars <- c("date_onset",
              "date_hospitalisation",
              "date_outcome",
              "date_infection"
              )
DateVars
## [1] "date_onset"           "date_hospitalisation" "date_outcome"        
## [4] "date_infection"

Method 2

# Method 2: Search for date columns
DateVars <- as.character(tidyselect::vars_select(names(linelist), matches("date|Date|dt")))
DateVars
## [1] "date_infection"       "date_onset"           "date_hospitalisation"
## [4] "date_outcome"         "date_death"

#Note: other search tool options within vars_select include contains() ends_with(), starts_with(), or one_of()

Ensure date variables are date class

Verify

Verify that each variable was successfully converted to date class by printing statistics and a quick histogram for each one.

# To verify successful conversion of date variables

# Creates list of column numbers of date variables 
varNums <- c()
for (varName in DateVars) {
  varNum <- match(varName, names(linelist))
  varNums <- c(varNums, varNum)
}

# Produce output for each date variable converted
for (varNum in varNums) {
  varName <- names(linelist)[varNum]         # get name of variable
  class <- class(linelist[, varNum])                  # get class
  missing <- sum(is.na(linelist[, varNum]))  # get number missing values
  hist(linelist[, varNum],                   # histogram
       breaks = 50,
       main = paste0("Histogram of: ", varName, ", Class: ", class, ", Missing: ", missing),
       xlab = varName)  
  }

incidence package

Below are tabs on using the “incidence” package

Outline

This section shows variations on the epicurve using the incidence package

#Determine the number of observations that are missing a date of onset

# Number of observations
n_linelist <- nrow(linelist)
n_linelist
## [1] 5888

# number missing onset_date and cannot be graphed
missing_onset <- nrow(linelist[is.na(linelist$date_onset),])
missing_onset
## [1] 249

Simple

These are simple epicurves using the incidence package. The epicurve is assigned to the object “epicurve”, which is then plotted. Remember that incidence::plot() is different to base::plot()

The interval defines how the observations are grouped. Options are all those in the package aweek, including but are not limited to:
* “Monday week” * “2 Monday weeks” * “Sunday week”
* “MMWRweek” (starts on Sunday)
* “Month”
* “Quarter”
* “Year”

First date and last date can also be specified.

# incidence object is created, with data aggregated at one day intervals
epicurve_daily   <- incidence::incidence(linelist$date_onset, interval = "day")

# If weekly, you can specific the start day
epicurve_weekly  <- incidence::incidence(linelist$date_onset, interval = "Monday week")
epicurve_3weekly  <- incidence::incidence(linelist$date_onset, interval = "3 weeks")

# Monthly
epicurve_monthly <- incidence::incidence(linelist$date_onset, interval = "month")

# Plot the incidence object
plot(epicurve_daily)
plot(epicurve_weekly)
plot(epicurve_3weekly)
plot(epicurve_monthly)

Aesthetics

Behind the scenes, incidence is using ggplot(), so you can add aesthetic themes and other lines using the ggplot syntax.

# Set theme elements using ggplot syntax
epicurve_theme <- ggplot2::theme(
  axis.text.x = element_text(angle = 45, hjust = 1, vjust = 1), 
  legend.title = element_blank(),
  panel.grid.major.x = element_line(color = "grey60", linetype = 3),
  panel.grid.major.y = element_line(color = "grey60", linetype = 3)
)

# Sets labels using ggplot syntax
epicurve_labels <- labs(x = "Week", 
                        y = "Cases (n)", 
                        title = "H5N7 cases by week of onset",
                        caption = paste0("Source: Linelist data from: ", data_date, "; ", missing_onset, " are missing date of onset and not shown."))
                       


# plot the epicurve with aesthetics
nice_plot <- plot(epicurve_weekly, show_cases = TRUE, border = "black", n_breaks = nrow(epicurve_weekly)) + 
  scale_y_continuous(expand = c(0, 0)) +  # set origin for axes
  # add labels
  epicurve_labels +
  # add theme
  epicurve_theme
nice_plot

# Modify nice_plot to show only 6 breaks in the x-axis 
nice_plot + scale_x_incidence(epicurve_weekly, n_breaks = 6)

By descriptor

Now differentiating the cases by gender, using the groups = argument in the incidence command

# Create epiweek object, with counts grouped by gender
epicurve_weekly_gender <- incidence(linelist$date_onset, 
                               interval = "week", 
                               groups = linelist$gender,
                               na_as_group = FALSE)   # Prevents missing values from being assigned their own group


# Plot the epicurve
# Note: Remove the boxes around each case as it makes gender colours hard to see! (show_cases = FALSE)
nice_plot <- plot(epicurve_weekly_gender, show_cases = FALSE, border = "black", n_breaks = nrow(epicurve_weekly_gender)) + 
  
  # add labels (defined in previous section)
  epicurve_labels +
  
  # add theme elements
  epicurve_theme

nice_plot

Filtered

To filter data, This version is filtered to only show data from a specific province.

# filter the dataset and pass it to the incidence() function
Connaught_data <- filter(linelist, hospital == "Connaught Hospital")

epicurve_Connaught <- incidence(Connaught_data$date_onset,
                                interval = "week",
                                groups = Connaught_data$gender)


# Re-sets labels, changing title to reflect subset
epicurve_labels <- labs(x = "Week", 
                        y = "Cases", 
                        title = "Ebola cases by week of onset in Connaught Hospital",
                        caption = paste0("Source: Linelist data from: ", data_date, "; ", missing_onset, " are missing date of onset and not shown."))
                       

# plot as before
plot(epicurve_Connaught, show_cases = TRUE, border = "grey") +
     
  # add labels (defined in previous section)
  epicurve_labels +
  
  # add theme elements
  epicurve_theme

ggplot2

Below are tabs on using “ggplot2” package

Daily and weekly

# Daily case counts 
###################
plot_daily <- ggplot(linelist, aes(x = date_onset)) + 
  
  # stacked bars, bined by day (1 days)
  stat_bin(binwidth = 1, position="stack") 

print(plot_daily)


# Weekly case counts 
###################
plot_weekly <- ggplot(linelist, aes(x = date_onset)) + 
  
  # stacked bars, bined by week (7 days)
  stat_bin(binwidth = 7, position="stack", fill = "brown") 

print(plot_weekly)

With aesthetics

# Preparation
#############
# Create epiweek variable. Factor argument automatically includes all weeks in span. Numeric shows just the week number.
linelist$epiweek <- aweek::date2week(linelist$date_onset, factor = TRUE, numeric = TRUE)

# Calculate maximum number of cases in an epiweek, to get the maximum y-axis height (also helps with uniformity in multiple plots)
ymax <- max(summary(factor(linelist$epiweek), maxsum = length(linelist$epiweek)))

# Weekly case counts 
###################
plot_weekly <- ggplot(linelist, aes(x = date_onset)) + 
  
  # stacked bars, bined by week (7 days)
  stat_bin(binwidth = 7, position = "stack", fill = "grey", color = "black") +
  
  # X-axis 21-day labels
  scale_x_date( 
    # Sets date label breaks as every 3 weeks from Monday before the first case
    breaks = function(x) seq.Date(from = min(linelist$date_onset, na.rm = T), to = max(linelist$date_onset, na.rm=T), by = "1 week"),
    
    # axis limits determined by max/min + buffer
    limits = c((min(linelist$date_onset, na.rm = T) - 8), (max(linelist$date_onset, na.rm = T) + 8)), 
    
    # displays as date number, then abbreviated month (e.g. 12 Oct)
    date_labels = "%d-%b",   
    
    # sets origin at (0,0)
    expand = c(0,0)) +                                
  
  # Y-axis breaks every 5 cases
  scale_y_continuous(breaks = seq(0, ymax, 5),
                     limits = c(0, ymax),
                     expand = c(0, 0)) +
  
  # Theme specifications (axis, text, etc.)
  theme(# title
        plot.title = element_text(size=20, hjust= 0, face="bold"),   # title size, font, bold
        # axes
        axis.text.x = element_text(angle=90, vjust=0.5, hjust=1),
        axis.text = element_text(size=12),
        axis.title = element_text(size=14, face="bold"),
        axis.line = element_line(colour="black"),
        # background
        panel.grid.major = element_blank(),
        panel.grid.minor = element_blank(),
        panel.background = element_blank(),
        # caption (italics, on right side)
        plot.caption = element_text(hjust = 0, face = "italic")
        ) +
  
  guides(fill = guide_legend(reverse = TRUE,                   # Orders Non-active zones at end of legend
                             override.aes = list(size = 0.2),
                             ncol = 2)) +                        # Number of legend columns
  
  labs(x = "Week of illness onset", 
       y = "Number of cases",
       subtitle = "subtitle here",
       caption = paste0(nrow(linelist),
                        " confirmed and probable cases, reported as of ", data_date, ". ",
                        missing_onset, " cases missing date of onset and not shown.")) +
  
  ggtitle("Epidemic curve")


# print
print(plot_weekly)

By category

Colored by a category

# Setup
########
# Two known classes (select colors from colorbrewer2.org)
colors_overall = c("#d95f02",  # 
                   "#1b9e77",
                   "#7570b3")  #    

# Order sex variable by reverse # of cases, so plot stacks with smallest # of cases at top
linelist$gender <- factor(linelist$gender, 
                         levels = levels(fct_rev(fct_infreq(linelist$gender))))

# Calculates maximum yaxis height for uniformity between the two graphs
ymax <- max(summary(factor(linelist$epiweek), maxsum = length(linelist$epiweek)))

# Number missing onset_date and cannot be graphed
missing_onset <- nrow(linelist[is.na(linelist$date_onset),])



# PLOT - BY ONSET DATE
######################
plot_defined_cats <- ggplot(linelist, aes(x = date_onset, fill = gender)) + 
  
  # stacked bars, width of 7 days
  stat_bin(binwidth = 7, position = "stack") +
  
  # Colors and labels of confirmed/probable
  scale_fill_manual(values = rev(colors_overall),
                    labels = str_to_sentence(levels(factor(linelist$gender)))) +
  
  # X-axis scale labels (not aggregation, just the labels)
  scale_x_date(# Sets date label breaks as every week
    breaks = function(x) seq.Date(from = min(linelist$date_onset, na.rm = T), to = max(linelist$date_onset, na.rm = T), by = "1 week"),
    limits = c((min(linelist$date_onset, na.rm=T)), (max(linelist$date_onset, na.rm = T))), # axis limits determined by max/min + buffer
    date_labels = "%d-%b",   # displays as date # then abbreviated month (e.g. 12 Oct)
    expand = c(0, 0)) +                                # sets origin at (0,0)
  
  # Y-scale in breaks, up to the ymax previously defined
  scale_y_continuous(breaks = seq(0, 500, 5), limits = c(0, ymax), expand=c(0, 0)) +
  
  # Themes for axes, titles, background, etc.
  theme(plot.title       = element_text(size=20, hjust=0.5, face="bold"),
        axis.text        = element_text(size=12),
        axis.title       = element_text(size=14, face="bold"),
        panel.grid.major = element_blank(),
        panel.grid.minor = element_blank(),
        panel.background = element_blank(),
        axis.line        = element_line(colour = "black"),
        axis.text.x      = element_text(angle=90, vjust=0.5, hjust=1)) +
  
  # Legend specifications
  theme(legend.title           = element_blank(),
        legend.justification   = c(0, 1), 
        legend.position        = c(0.09, 0.98),
        legend.background      = element_blank(),
        legend.text            = element_text(size = 12)) +
  guides(fill = guide_legend(reverse = TRUE, override.aes = list(size = 0.2))) +
  
  # Axis and caption labels
  labs(x = "Week of illness onset",
       y = "Number of Cases",
       caption = paste(missing_onset,"cases were missing onset date and are not included in the onset graph")) +
  
  # Title
  ggtitle("Cases by week of illness onset")

# print
print(plot_defined_cats)

By active area

# PARAMETERS
#############

# Maximum y-value for epiweek (this will be larger than necessary because of missing onset dates)
ymax <- max(table(linelist$epiweek))

# Number missing onset_date and cannot be graphed
missing_onset <- nrow(filter(linelist, is.na(date_onset)))


# SETUP - ACTIVE/NON-ACTIVE ZONES
#################################
# List of "active" zones with a case in the date range
active_zones <- unique(linelist$province[which(linelist$date_onset > (data_date - 90))])
active_zones

# Table of active zones and their overall number of cases (for ordering their stacked appearance)
order_table <- linelist %>%
  filter(province %in% active_zones) %>%
  group_by(province) %>%
  summarise(cases = n())
order_table

# Create TRUE/FALSE variable for "active" health zones
linelist$active_zone <- ifelse(linelist$province %in% active_zones, TRUE, FALSE)

# Create list of non-active HZ names for bottom of plot
other_zone_names <- unique(sort(linelist$province[linelist$active_zone == FALSE]))

# Make variable for graph categories, including a level for "non-active" zones
linelist$graph_zone <- factor(case_when(
  
  # Value assignments
  # Non-active zones
  linelist$active_zone == FALSE    ~ "Non-active zones",
  # All others are assigned their names, capitalized
  TRUE  ~ stringr::str_to_title(linelist$province)),
  
  # Order of variable levels
  levels = c(
    # "Non-active zones" is first level
    "Non-active zones",  
    
    # Orders active zones by their frequency in linelist, reversed, so most-affected zones are on the BOTTOM of plot
    str_to_title(rev(levels(fct_infreq(as.factor(linelist$province[linelist$active_zone == TRUE])))))))  


table(linelist$graph_zone, useNA = "ifany")


# COLORS
########
# Number of unique values in graph_zone variable, minus 1 (for non-active, which is added later as grey (#cccccc))
colors_needed <- length(unique(linelist$graph_zone, na.rm=T)) - 1 

# List of possible colors (see colorbrewer2.com, qualitative scheme)
colors_linelist = c(#"#cccccc", # first = non-active grey color
                "#1b9e77", # turquoise green
                "#ff7f00", # orange
                "#ffff33", # yellow
                "#6a3d9a", # purple
                "#b15928", # brown
                "#1f78b4", # blue
                "#e31a1c", # red,
                "#fb9a99", # pink
                "#b2df8a", # light green 
                "#cab2d6", # light purple
                "#a6cee3", # light blue
                "#fdbf6f", # beige
                "#33a02c"  # green
)

# Reduce number of colors to only the number needed
colors_linelist <- c("#cccccc", rev(colors_linelist[1:colors_needed]))


# MAKE GRAPH
#############
plot_overall <- ggplot(linelist, aes(x = date_onset, fill = graph_zone)) + 
  
  # stacked bars, bined by week (7 days)
  stat_bin(binwidth = 7, position = "stack") +
  
  # Fill of bars
  scale_fill_manual(values = colors_linelist, 
                    labels = str_to_sentence(levels(factor(linelist$graph_zone)))) +
  
  # X-axis 21-day labels
  scale_x_date( # Sets date label breaks as every 3 weeks from Monday before the first case
    breaks = function(x) seq.Date(from = min(linelist$date_onset, na.rm = T), to = max(linelist$date_onset, na.rm = T), by = "1 week"),
    limits = c((min(linelist$date_onset, na.rm = T) - 8), (max(linelist$date_onset, na.rm = T) + 8)), # axis limits determined by max/min + buffer
    date_labels = "%d-%b",   # displays as date number, then abbreviated month (e.g. 12 Oct)
    expand = c(0,0)) +                                # sets origin at (0,0)
  
  # Y-axis breaks every 5 cases
  scale_y_continuous(breaks = seq(0, ymax, 5),
                     limits = c(0, ymax),
                     expand = c(0, 0)) +
  
  # Theme specifications (axis, text, etc.)
  theme(plot.title = element_text(size = 20, hjust = 0, face = "bold"),   # title size, font, bold
        axis.text.x = element_text(angle=90, vjust=0.5, hjust=1),
        axis.text = element_text(size=12),
        axis.title = element_text(size=14, face="bold"),
        axis.line = element_line(colour="black"),
        panel.grid.major = element_blank(),
        panel.grid.minor = element_blank(),
        panel.background = element_blank(),
        plot.caption = element_text(hjust = 0, face = "italic")
        ) +
  
  # Legend specifications
  theme(legend.title = element_blank(),                        # No legend title
        legend.position = c(0.20, 0.85),                         # placement of legend
        legend.background = element_blank(),                     # legend background   
        legend.text = element_text(size=12)) +                 # legend text size
  
  guides(fill = guide_legend(reverse = TRUE,                   # Orders Non-active zones at end of legend
                             override.aes = list(size = 0.2),
                             ncol = 2)) +                        # Number of legend columns
  
  labs(x = "Week of illness onset", 
       y = "Number of cases",
       subtitle = "Health zones with cases in the last 42 days specified by color",
       caption = paste0(nrow(linelist),
                        " confirmed and probable cases, reported as of ", data_date, ". ",
                        missing_onset, " cases missing date of onset and not shown.",
                        "\nNon-active zones include: ", str_to_title(toString(unique(linelist$province[linelist$active_zone == FALSE]))))) +
  
  ggtitle("Epidemic curve by active health zones")


# print
plot_overall

By grouped areas

#SETUP
#############
# Filter to health zone of interest
zone_data <- linelist

# Number missing onset_date and cannot be graphed
missing_onset <- nrow(filter(linelist, is.na(date_onset)))

# Assign health area groups (individual for HAs of interest, groups others together)
linelist$graph_areas <- factor(case_when(
  linelist$province == "Shanghai"    ~ "Shanghai",
  linelist$province == "Jiangsu"     ~ "Jiangsu",
  linelist$province == "Zhejiang"    ~ "Zhejiang",
  TRUE                                ~ "Other (10)"
),
# Levels part of the factor function assigns order of appearance
levels = c(                         
  "Other (10)",
  "Shanghai",
  "Jiangsu",
  "Zhejiang"
)
)

# checks
table(linelist$graph_areas, useNA = "ifany")

# Color assignments
colors_needed <- length(unique(linelist$graph_areas, na.rm=T)) - 1 # number of colors needed

# list of colors
colors_aire = c("#a6cee3", 
                           "#1f78b4",
                           "#b2df8a", 
                           "#33a02c",
                           "#fb9a99",
                           "#e31a1c",
                           "#fdbf6f",
                           "#ff7f00",
                           "#cab2d6",
                           "#6a3d9a",
                           "#ffff99",
                           "#b15928"
                           )

# Reduce number of colors to only the number needed
colors_aire <- c("#cccccc", rev(colors_aire[1:colors_needed]))


# Plot of province
#####################################
plot <- ggplot(linelist, aes(x = date_onset, fill = graph_areas)) + 
  
  stat_bin(binwidth = 7, position="stack") +
  
  scale_fill_manual(values = colors_aire, labels = str_to_sentence(levels(factor(linelist$graph_areas)))) +
  
  scale_x_date(date_breaks = "1 week", date_labels = "%d-%b", limits = c((min(linelist$date_onset, na.rm = T) - 8), (max(linelist$date_onset, na.rm = T) + 8)), expand=c(0,0)) + # I used the date onset variable here so x axes will be the same
  
  scale_y_continuous(breaks = seq(0, 500, 5), limits = c(0, 35), expand = c(0, 0)) +
  
  theme(plot.title = element_text(size = 20, hjust = 0.5, face = "bold"),
        plot.caption = element_text(hjust = 0, face = "italic"),
        
        panel.grid.major = element_blank(),
        panel.grid.minor = element_blank(),
        panel.background = element_blank(),
        
        
        axis.line = element_line(colour = "black"),
        axis.text = element_text(size = 12),
        axis.text.x = element_text(angle = 90, vjust = 0.5, hjust = 1),
        axis.title = element_text(size = 14, face = "bold"),
        
        legend.title = element_blank(),
        legend.justification = c(0,1), 
        legend.position = c(0.05, 1),
        legend.background = element_blank(),
        legend.text = element_text(size = 12)) +
  
  guides(fill = guide_legend(reverse = TRUE, override.aes = list(size = 0.2), ncol = 4)) +
  
  labs(x="Week of illness onset", 
       y="Number of cases",
       subtitle = "",
       caption = paste0(nrow(zone_data), " confirmed and probable cases, as of ", data_date, ". \n", missing_onset, " cases excluded due to missing date of onset.")) +
  
  ggtitle("Cases of influenza, by province")

plot

By “wave” in time

# Define the waves
##################
# zone_data <- filter(linelist, zone_de_sante == "mabalako")
# 
# zone_data$wave <- case_when(
#   zone_data$date_onset >= as.Date("2018-03-01") &
#     zone_data$date_onset < as.Date("2018-10-25")     ~ "Wave 1",
#   
#   zone_data$date_onset >= as.Date("2018-10-25") &
#     zone_data$date_onset < as.Date("2019-02-01")     ~ "Wave 2",
#   
#   zone_data$date_onset >= as.Date("2019-02-01") &
#     zone_data$date_onset < as.Date("2019-09-15")     ~ "Wave 3",
#   
#   zone_data$date_onset >= as.Date("2019-09-15")      ~ "Wave 4",
#   
#   TRUE ~ NA_character_
# )
# 
# table(is.na(zone_data$date_onset))
# table(zone_data$wave, useNA = "always")
# 
# 
# # Color assignments
# colors_needed <- length(unique(zone_data$wave, na.rm=T)) # number of colors needed
# 
# # list of colors
# colors_aire = c("#a6cee3", 
#                            "#1f78b4",
#                            "#b2df8a", 
#                            "#33a02c",
#                            "#fb9a99",
#                            "#e31a1c",
#                            "#fdbf6f",
#                            "#ff7f00",
#                            "#cab2d6",
#                            "#6a3d9a",
#                            "#ffff99",
#                            "#b15928"
#                            )
# 
# # Reduce number of colors to only the number needed
# colors_aire <- c(rev(colors_aire[1:colors_needed]))
# 
# 
# # Plot of health zone colored by wave
# #####################################
# plot_Mabalako <- ggplot(zone_data, aes(x = date_onset, fill = wave)) + 
#   
#   stat_bin(binwidth = 7, position = "stack") +
#   
#   scale_fill_manual(values = rev(colors_aire), labels = str_to_sentence(levels(factor(zone_data$wave)))) +
#   
#   scale_x_date(date_breaks = "21 days", date_labels = "%d-%b",
#                limits = c((min(zone_data$date_onset, na.rm = T) - 8), (max(zone_data$date_report, na.rm = T) + 8)), expand = c(0,0)) +
#   
#   scale_y_continuous(breaks = seq(0, 500, 5), limits = c(0, 35), expand = c(0, 0)) +
#   
#   theme(text = element_text(family = "Segoe Condensed"),
#         axis.text.x = element_text(angle = 90, vjust = 0.5, hjust = 1),
#         axis.text = element_text(size = 12),
#         axis.title = element_text(size = 14, face = "bold"),
#         axis.line = element_line(colour = "black"),
#         
#         plot.title = element_text(size = 20, hjust = 0.5, face = "bold"),
#         plot.caption = element_text(hjust = 0, face = "italic"),
#         
#         panel.grid.major = element_blank(),
#         panel.grid.minor = element_blank(),
#         panel.background = element_blank(),
#         
#         legend.title = element_blank(),
#         legend.justification = c(0,1), 
#         legend.position = c(0.75, 0.98),
#         legend.background = element_blank(),
#         legend.text = element_text(size=12)) +
#   
#   guides(fill = guide_legend(reverse = TRUE, override.aes = list(size = 0.2), ncol = 1)) +
#   
#   labs(x="Week of illness onset", 
#        y="Number of cases",
#        subtitle = "",
#        caption = paste0(nrow(zone_data), " confirmed and probable cases, as of ", data_date, ". \n", missing_onset, " cases excluded due to missing date of onset and 16 excluded due to uncertain health zone of report.")) +
#   
#   ggtitle("Four waves of EVD in Mabalako health zone")
# 
# plot_Mabalako
# 
# 
# # Produce table describing each wave
# ####################################
# table <- zone_data %>%
#   select("aire_de_sante", "wave", "community_death", "date_onset", "cte_date", "epicasedef", "community_death", "contact_registered", "contact_surveilled") %>%
#   group_by(wave) %>%
#   summarise(first_onset       = min(date_onset, na.rm = T),
#             last_admission    = max(cte_date, na.rm = T),
#             n                 = n(),
#             confirmed         = sum(epicasedef == "confirmed"),
#             community_deaths  = paste0(sum(community_death    == 1), 
#                                        " (", round(100*sum(community_death == 1)/confirmed),"%)"),
#             reg_contacts      = paste0(sum(contact_registered == "yes"),
#                                        " (", round(100*sum(contact_registered == "yes")/confirmed),"%)"),
#             surv_contacts     = paste0(sum(contact_surveilled == "yes"),
#                                        " (", round(100*sum(contact_surveilled == "yes")/confirmed),"%)"),
#             top               = paste(toupper(names(sort(table(aire_de_sante),decreasing=TRUE)[1:3])), collapse=", ",
#                                       round(100*(sort(table(aire_de_sante),decreasing=TRUE)[1:3]/confirmed)), "%"),
#             health_areas      = paste(toupper(unique(aire_de_sante)), collapse=', ') 
#             )
# 
# kable(table)

Aggregated data

Situation

Often you do not have linelist data, but instead daily case counts from facilities, districts, etc. You can plot these in an epidemiological curve, but the code will be slightly different.

This section will utilize the counts_data dataset that was imported earlier, in the data preparation section.

Note: The incidence package does not support aggregate data

Clean dates

As before, we must ensure date variables are correctly classified.

# Convert Date variable to Date class
class(count_data$date_hospitalisation)
## [1] "POSIXct" "POSIXt"
count_data$date_hospitalisation <- as.Date(count_data$date_hospitalisation)

Create weeks

# Create epiweek variable
# aweek weeks are also stored as dates, facilitating better display manipulation
count_data$epiweek <- aweek::date2week(count_data$date_hospitalisation,      # use the Date variable
                                        week_start = "Monday", # epiweek begins on Monday
                                        floor_day = TRUE,      # only display year and week #
                                        factor = TRUE)         # expand to include all possible weeks

Clean dates

ggplot(data = count_data, aes(x = as.Date(epiweek), y = n_cases, group = hospital, fill = hospital))+
     geom_bar(stat = "identity")+
     
     # LABELS for x-axis
     scale_x_date(date_breaks = "1 month",  # displays by month
                  date_labels = '%b%d\n%Y')+  #labeled by month with year below
     
     # Choose color palette (uses RColorBrewer package)
     scale_fill_brewer(palette = "Pastel1")+      
     
     # Theme specifications (axis, text, etc.)
     theme(
          # title
          plot.title = element_text(size=20, hjust= 0, face="bold"),   # title size, font, bold
          # axes
          axis.text.x = element_text(angle=0, vjust=0.5, hjust=1),
          axis.text = element_text(size=12),
          axis.title = element_text(size=14, face="bold"),
          axis.line = element_line(colour="black"),
          # background
          panel.grid.major = element_blank(),
          panel.grid.minor = element_blank(),
          panel.background = element_blank(),
          # caption (italics, on right side)
          plot.caption = element_text(hjust = 0, face = "italic"))+
     
     # labels
     labs(x = "Week of report", 
          y = "Number of cases",
          subtitle = "Cases aggregated by week and shown by hospital",
          caption = "Data source: XXXXX")+
     
     ggtitle("Epidemic curve of disease X in fictional location")

Dual-axis

Although there are fierce discussions about the validity of this within the data visualization community, many supervisors want to see an epicurve or similar chart with a percent overlaid with a second axis.

In ggplot it is very difficult to do this, except for the case where you are showing a line reflecting the proportion of a category shown in the bars below.

Second axis

This uses the linelist dataset

TODO not complete yet

library(reshape2)
# group the data by week, summarize counts by group (gender)
linelist_week <- linelist %>%
     mutate(onset_epiweek = aweek::date2week(date_onset, floor_day = TRUE, factor = TRUE)) %>%
     group_by(onset_epiweek) %>%
     summarize(num_male = sum(gender == "m"),
               num_female = sum(gender == "f"),
               pct_male = round(100*(num_male / n())),
               med_age  = median(as.numeric(age), na.rm=T)
               ) 
# remove pct and melt
linelist_week_melted <- linelist_week %>%
     select(-c("pct_male", "med_age")) %>%
     melt(id.vars = c("onset_epiweek"))

# merge together (multiple of the same values in week will attach to melted)
linelist_week_melted <- merge(linelist_week_melted,
                              linelist_week,
                              by = "onset_epiweek")

second_axis <- ggplot(linelist_week_melted,
                      aes(x = as.Date(onset_epiweek),
                          y = value, group = variable,
                          fill = variable)) + 
  
  # bars
  geom_bar(stat = "identity")+
  
  # Colors and labels of confirmed/probable
  scale_fill_manual(values = c("blue", "red"),
                    labels = str_to_sentence(levels(factor(linelist_week_melted$variable)))) +
  
  geom_line(mapping = aes(y = pct_male, color = "% male"), size = 0.5) +
     
  scale_color_manual(values = "black")+
     
  scale_y_continuous(sec.axis = sec_axis(~(./sum(linelist_week_melted$value, na.rm = T)*100), name = "name here", breaks = seq(0, 100, 20)))+


  # X-axis scale labels (not aggregation, just the labels)
  scale_x_date(# Sets date label breaks as every week
    breaks = function(x) seq.Date(from = min(linelist$date_onset, na.rm = T), to = max(linelist$date_onset, na.rm = T), by = "1 week"),
    limits = c((min(linelist$date_onset, na.rm=T)), (max(linelist$date_onset, na.rm = T))), # axis limits determined by max/min + buffer
    date_labels = "%d-%b",   # displays as date # then abbreviated month (e.g. 12 Oct)
    expand = c(0, 0)) +                                # sets origin at (0,0)
  
  # Y-scale in breaks, up to the ymax previously defined
  scale_y_continuous(breaks = seq(0, 500, 5), limits = c(0, ymax), expand=c(0, 0)) +
  
  # Themes for axes, titles, background, etc.
  theme(plot.title       = element_text(size=20, hjust=0.5, face="bold"),
        axis.text        = element_text(size=12),
        axis.title       = element_text(size=14, face="bold"),
        panel.grid.major = element_blank(),
        panel.grid.minor = element_blank(),
        panel.background = element_blank(),
        axis.line        = element_line(colour = "black"),
        axis.text.x      = element_text(angle=90, vjust=0.5, hjust=1)) +
  
  # Legend specifications
  theme(legend.title           = element_blank(),
        legend.justification   = c(0, 1), 
        legend.position        = c(0.09, 0.98),
        legend.background      = element_blank(),
        legend.text            = element_text(size = 12)) +
  guides(fill = guide_legend(reverse = TRUE, override.aes = list(size = 0.2))) +
  
  # Axis and caption labels
  labs(x = "Week of illness onset",
       y = "Number of Cases",
       caption = paste(missing_onset,"cases were missing onset date and are not included in the onset graph")) +
  
  # Title
  ggtitle("Cases by week of illness onset")

second_axis

# print
print(plot_defined_cats)

Resources

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Scatterplots

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Overview

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This tab should include:

• Textual overview of the purpose of this page
  
• Small image showing outputs

Preparation

Keep the title of this section as “Preparation”.
Data preparation steps such as:

• Loading dataset
  
• Adding or changing variables
  
• melting, pivoting, grouping, etc.

base R

plot(linelist$age)

ggplot2

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Boxplots

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Overview

Boxplots can be created with:

• the boxplot() function from the graphics package (installed automatically with base R), or
  
• the ggplot() function from the ggplot2 package

Preparation

Keep the title of this section as “Preparation”.
Data preparation steps such as:

• Loading dataset
  
• Adding or changing variables
  
• melting, pivoting, grouping, etc.

linelist <- linelist %>% 
  mutate(age = as.numeric(age))

boxplot()

Some options with boxplot() shown below are:

• boxplots by group (color specification optional)
  
• violin plots
  
• boxplot width proportional to sample size
  
• Horizontal

# boxplot of one numeric variable
boxplot(linelist$age,                 # numeric variable
        main="boxplot",               # main title
        xlab="Suppliment and Dose")   # x-axis label


# by group (formula style)
boxplot(age ~ gender, data=linelist, notch=TRUE, main="boxplot", xlab="Suppliment and Dose")

You can have multiple levels of group (e.g. age by outcome AND gender)
Notched “violin plots” are possible. The notch represents the median and X around it (TODO)

# By subgroup (age by outcome AND gender)
boxplot(age ~ outcome * gender,
        data=linelist,
        col=c("gold","darkgreen"),            # colors, in a vector
        main="Boxplot by Outcome and Gender", # main title
        xlab="Suppliment and Dose")           # x-axis label

# Notched (violin plot), and varying width
boxplot(age ~ outcome * gender,
        data=linelist,
        notch=TRUE,      # notch at median
        varwidth = TRUE, # width varying by sample size
        col=(c("gold","darkgreen")),
        main="Notched boxplot, width varying by sample size",
        xlab="Suppliment and Dose")

# Horizontal
boxplot(age~outcome,
        data=linelist,
        horizontal=TRUE,  # flip to horizontal
        col=(c("gold","darkgreen")),
        main="Horizontal boxplot",
        xlab="Suppliment and Dose")

ggplot()

Some options with ggplot() shown below are:

• boxplots by group (color specification optional)
  
• violin plots
  
• boxplot width proportional to sample size
  
• Horizontal

# Simple boxplot of one numeric variable
ggplot(data = linelist, aes(y = age))+  # only y variable given (no x variable)
  geom_boxplot()+
  ggtitle("Simple ggplot() boxplot")

# By group
ggplot(data = linelist, aes(y = age,         # numeric variable
                            x = outcome,      # group variable
                            fill = outcome))+ # fill variable (color of boxes)
  geom_boxplot()+                            # create the boxplot
  ggtitle("ggplot() boxplot by gender")      # main title

# Removing missing values, and add color
ggplot(data = linelist %>% filter(!is.na(outcome)), # dataset piped through a filter to retain rows where gender is not missing
       aes(y = age, x = outcome, fill= outcome))+    # boxes filled according to gender value
  geom_boxplot()+
  ggtitle("ggplot() boxplot by gender (missing excluded)")

To examine by subgroups, use facet_wrap() (for more see section on ggplot tips).

# By subgroup
ggplot(data = linelist %>% filter(!is.na(gender)), # dataset piped through a filter to retain rows where gender is not missing
       aes(y = age, x = outcome, fill=outcome))+
  geom_boxplot()+
  ggtitle("A ggplot() boxplot")+
  facet_wrap(~gender)

“Violin plots” can be made simply or very complex:

# Violin plots
ggplot(linelist, aes(x=age, y=outcome, fill = outcome)) + 
  geom_violin(trim=FALSE)


# Vertical violin plot
ggplot(linelist, aes(x=age, y=outcome, fill = outcome)) + 
  geom_violin(trim=FALSE)+
  coord_flip()


# Add jittered points
ggplot(linelist, aes(x=age, y=outcome, fill = outcome)) + 
  geom_violin(trim=FALSE)+
  coord_flip()+ 
  geom_jitter(shape=16,                      # points
              position=position_jitter(0.2)) # jitter permissible to avoid point overlap

Resources

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Moving averages

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Overview

Calculating Visualizing

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• Textual overview of the purpose of this page
  
• Small image showing outputs

Preparation

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Data preparation steps such as:

• Loading dataset
  
• Adding or changing variables
  
• melting, pivoting, grouping, etc.

sub-tab 1

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sub-tab 2

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Option 1

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Transmission Chains

Overview

The primary tool to visualize and analyze transmission chains is the package epicontacts, developed by the folks at RECON.

Preparation

Visualization

links <- epicontacts::make_epicontacts(linelist = mers_korea_2015$linelist,
                                       contacts = mers_korea_2015$contacts, 
                                       directed = TRUE)
# plot without time
plot(links,
     selector = FALSE,
     height = 700,
     width = 700)

And in a transmission tree, with date of onset on the x-axis:

Note: this currently requires installing a development version of epicontacts from github… @ttree

# plot with date of onset as x-axis
plot(sim,
     x_axis = 'onset',
     height = 700,
     width = 700,
)

Analysis

summary(links)
## 
## /// Overview //
##   // number of unique IDs in linelist: 162
##   // number of unique IDs in contacts: 97
##   // number of unique IDs in both: 97
##   // number of contacts: 98
##   // contacts with both cases in linelist: 100 %
## 
## /// Degrees of the network //
##   // in-degree summary:
##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
##  0.0000  0.0000  1.0000  0.6049  1.0000  3.0000 
## 
##   // out-degree summary:
##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
##  0.0000  0.0000  0.0000  0.6049  0.0000 38.0000 
## 
##   // in and out degree summary:
##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
##    0.00    0.00    1.00    1.21    1.00   39.00 
## 
## /// Attributes //
##   // attributes in linelist:
##  age age_class sex place_infect reporting_ctry loc_hosp dt_onset dt_report week_report dt_start_exp dt_end_exp dt_diag outcome dt_death
## 
##   // attributes in contacts:
##  exposure diff_dt_onset

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Abberation detection

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Overview

Endemic corridor analysis Detecting spikes in syndromic/routine surveillance

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sub-tab 1

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Time series analysis

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Preparation

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sub-tab 1

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sub-tab 2

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Option 1

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Survey analysis

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Overview

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Preparation

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• melting, pivoting, grouping, etc.

sub-tab 1

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sub-tab 2

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From data frame

Overview

Weighting

Random selection

Resources

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Age Standardization

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Overview

Why How When etc.

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sub-tab 1

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PHEindicatormethods package

DSR package

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Diagrams

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Patient flow

E.g. EVD patient “pathways” to outcome (via clinic or not, etc.)

HIV care continuum datasets? PreP datasets?

Alluvial

Sankey plots - show transitions among cohort over time, interrelatedness of groups Liza Coyer TODO

Clinical trials

Or papers in meta-analysis

Event timelines

E.g. border closures during COVID

Resources

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Age pyramids

Age pyramids can be useful to show patterns by age group. They can show gender, or the distribution of other characteristics.
These tabs demonstrate how to produce age pyramids using:

• Fast & easy: Using the apyramid package
  
• More flexible: Using ggplot()

Overview

via ggplot and via R4Epis methods TODO

Preparation

To make a traditional age/sex demographic pyramid, the data must first be cleaned in the following ways:

• The gender column must be cleaned.
  
• The age column must be numeric, cleaned, and grouped into age groups

Setup and Importing the data

First, load the packages required for this analysis:

pacman::p_load(rio,       # to import data
               here,      # to locate files
               tidyverse, # to clean, handle, and plot the data (includes ggplot package)
               apyramid   # a package dedicated to creating age pyramids
               ) 

Now import the data

linelist <- import(here("data", "fluH7N9_China_2013.csv"))

Clean values

Clean age values

Sometimes, age variables will import as class “character”. This occurs most often if there are non-numeric characters in some of the values, for example entries of “2 months”, or how in some countries commas are used in the decimals place (e.g. “4,5” to mean four and one half years)

In other circumstances, a numeric age variable may be adjacent to another “units” variable (values of either years or months). This will require additional cleaning to calculate a completely numeric age. You can find an example of this in the cleaning page (LINK).

#check the class of the linelist variable age
class(linelist$age)
## [1] "character"

# ensure class of age variable is numeric, by re-defining itself 
linelist$age <- as.numeric(linelist$age)

Now create age groups. There are a few ways to do this. The most simple way is using the base function cut(), which creates groups from a numeric variable.
First you give the numeric variable to be cut (age), the the breaks argument which is a vector (c()) of number break points.

By default, the grouping occurs so that the right/upper side is “open” and inclusive (and the left/lower side is “closed” or exclusive). The default labels use the notation “(A, B]”, which means the group does not include A (the lower break), but includes B (the upper break). You can reverse this behavior by providing the right argument and setting equal to FALSE (explanation below).

# Create new variable, by cutting the numeric age variable
linelist$age_group <- cut(linelist$age,
                          breaks = c(0, 5, 10, 15, 20, 30, 45, 100))

# tabulate the number of observations per group
table(linelist$age_group)
## 
##    (0,5]   (5,10]  (10,15]  (15,20]  (20,30]  (30,45] (45,100] 
##        4        3        1        0        5       20      101

You may want to verify that each age was assigned to the correct group. You can do this by cross-tabulating the age and age_group variables (names added for clarity). For example, you can check that the one 15-year patient was correctly assigned.

table(age = linelist$age, age_group = linelist$age_group, useNA = "always")
##       age_group
## age    (0,5] (5,10] (10,15] (15,20] (20,30] (30,45] (45,100] <NA>
##   2        1      0       0       0       0       0        0    0
##   4        3      0       0       0       0       0        0    0
##   6        0      1       0       0       0       0        0    0
##   7        0      1       0       0       0       0        0    0
##   9        0      1       0       0       0       0        0    0
##   15       0      0       1       0       0       0        0    0
##   21       0      0       0       0       1       0        0    0
##   25       0      0       0       0       1       0        0    0
##   26       0      0       0       0       2       0        0    0
##   27       0      0       0       0       1       0        0    0
##   31       0      0       0       0       0       2        0    0
##   32       0      0       0       0       0       2        0    0
##   34       0      0       0       0       0       1        0    0
##   35       0      0       0       0       0       2        0    0
##   36       0      0       0       0       0       2        0    0
##   37       0      0       0       0       0       2        0    0
##   38       0      0       0       0       0       5        0    0
##   41       0      0       0       0       0       1        0    0
##   43       0      0       0       0       0       2        0    0
##   45       0      0       0       0       0       1        0    0
##   47       0      0       0       0       0       0        1    0
##   48       0      0       0       0       0       0        3    0
##   49       0      0       0       0       0       0        1    0
##   50       0      0       0       0       0       0        2    0
##   51       0      0       0       0       0       0        2    0
##   52       0      0       0       0       0       0        1    0
##   53       0      0       0       0       0       0        2    0
##   54       0      0       0       0       0       0        6    0
##   55       0      0       0       0       0       0        2    0
##   56       0      0       0       0       0       0        6    0
##   57       0      0       0       0       0       0        1    0
##   58       0      0       0       0       0       0        3    0
##   59       0      0       0       0       0       0        1    0
##   60       0      0       0       0       0       0        4    0
##   61       0      0       0       0       0       0        2    0
##   62       0      0       0       0       0       0        4    0
##   64       0      0       0       0       0       0        4    0
##   65       0      0       0       0       0       0        4    0
##   66       0      0       0       0       0       0        3    0
##   67       0      0       0       0       0       0        3    0
##   68       0      0       0       0       0       0        4    0
##   69       0      0       0       0       0       0        5    0
##   70       0      0       0       0       0       0        1    0
##   72       0      0       0       0       0       0        3    0
##   73       0      0       0       0       0       0        1    0
##   74       0      0       0       0       0       0        5    0
##   75       0      0       0       0       0       0        2    0
##   76       0      0       0       0       0       0        3    0
##   77       0      0       0       0       0       0        2    0
##   78       0      0       0       0       0       0        1    0
##   79       0      0       0       0       0       0        5    0
##   80       0      0       0       0       0       0        3    0
##   81       0      0       0       0       0       0        2    0
##   83       0      0       0       0       0       0        2    0
##   84       0      0       0       0       0       0        1    0
##   85       0      0       0       0       0       0        1    0
##   86       0      0       0       0       0       0        2    0
##   87       0      0       0       0       0       0        1    0
##   89       0      0       0       0       0       0        1    0
##   91       0      0       0       0       0       0        1    0
##   <NA>     0      0       0       0       0       0        0    2

NOTE: Note that if you provide a highest break value that is too low, values may be excluded accidentally! You can write code that automatically adapts, by replacing the static number with the max() function, as below. Don’t forget to include na.rm argument to max() so that missing values are excluded from the maximum calculation (see LINK to description of max function).

linelist$age_group <- cut(linelist$age,
                          breaks = c(0, 5, 10, 15, 20, 30, 45, max(linelist$age, na.rm=T)))
table(linelist$age_group)
## 
##   (0,5]  (5,10] (10,15] (15,20] (20,30] (30,45] (45,91] 
##       4       3       1       0       5      20     101

It may be important to also include the argument include.lowest and set it equal to TRUE, so that any values of 0 are still included in the lowest group (this could apply if infants have been coded as age 0). Customized group labels can be added manually with the labels argument. Both are shown below:

linelist$age_group <- cut(linelist$age,
                          breaks = c(0, 5, 10, 15, 20, 30, 45, 100),
                          include.lowest = TRUE,
                          labels = c("0-5", "6-10", "11-15", "16-20", "21-30", "31-45", "46-100"))

table(linelist$age_group)
## 
##    0-5   6-10  11-15  16-20  21-30  31-45 46-100 
##      4      3      1      0      5     20    101

If you include the argument right and set it equal to TRUE, then the lower break points will be included in each group and the upper breaks will not be included in the group. Note how one patient moves from the 3rd group to the 4th group.
Note: The include.lowest argument will now apply to the highest break point (60), not the lowest (0).

linelist$age_group <- cut(linelist$age,
                          breaks = c(0, 5, 10, 15, 20, 30, 45, 100),                              # same breaks
                          right = FALSE,                                                         # change the inclusion            
                          include.lowest = TRUE,                                                 # same, but now applies to highest break
                          labels = c("0-4", "5-9", "10-14", "15-19", "20-29", "30-44", "45-100")) # now the labels must change

table(linelist$age_group)
## 
##    0-4    5-9  10-14  15-19  20-29  30-44 45-100 
##      4      3      0      1      5     19    102

If you want a fast way to make breaks and labels, you can use this:

# TO DO - NOT CORRECT 


# Make groups from 0 to 90 by 5
age_seq = seq(from = 0, to = 90, by = 5)

# Make labels for the above
age_lab = paste0(age_seq, "-", age_seq + 4)
length(age_seq)
## [1] 19
length(age_lab)
## [1] 19

# # Use these in the cut() command
# linelist$age_group <- cut(linelist$age,
#                           breaks = age_seq,
#                           labels = age_lab)

Clean gender values

Create a table of the values in the variable “Gender”

table(linelist$gender, useNA = "always")
## 
##    f    m <NA> 
##   39   95    2

Take the opportunity to make changes or clean any incorrect values.

apyramid package

The package apyramid allows you to quickly make an age pyramid. For more nuanced situations, see the tab on using ggplot() to make age pyramids. You can read more about the apyramid package by entering ?age_pyramid in your R console.

First we load the package (if not already done)

#load package
pacman::p_load(apyramid)

Now, using the cleaned dataset (see previous tabs), we can create an age pyramid with just one command.

• The data argument is set as the linelist dataframe
  
• The age_group argument (y-axis) is set to the name of your grouping variable (in quotes)
  
• The split_by argument (colors) in this case is set to our “gender” variable. You can also use other variables.

Note: If the split_by variable is bivariate (e.g. male/female, or yes/no), then the result will show up as a pyramid, otherwise, it will be presented as a facetted barplot with with empty bars in the background indicating the range of the un-facetted data set. Values of split_by will show up as labels at top of each facet.

apyramid::age_pyramid(data = linelist,
                      age_group = "age_group",
                      split_by = "gender")

By default, the bars display counts (not %), a dashed mid-line for each group is shown, the colors are green/purple, and missing values are not shown. These can all be adjusted, as shown below:

apyramid::age_pyramid(data = linelist,
                      age_group = "age_group",
                      split_by = "gender",
                      proportional = TRUE, # show percents, not counts
                      na.rm = FALSE        # show a bar for patients missing age
                      )

You can always add additional ggplot() commands to the plot using the standard ggplot() “+” syntax, such as aesthetic themes and label adjustments:

apyramid::age_pyramid(data = linelist,
                      age_group = "age_group",
                      split_by = "gender")+
  theme_minimal()+         # this is a standard ggplot() theme that simplifies the aesthetic background
  labs(y = "Counts",       # note that for age pyramids the x and y labels are switched.
       x = "Age Groups",   # Read more about this in the ggplot() age pyramid tab
       caption = "My data source and caption here",
       title = "Title of my plot",
       subtitle = "Subtitle but with \n2 lines...")+
  theme(axis.text = element_text(size = 10, face = "bold"),    # see ggplot() tips page for details
        axis.title = element_text(size = 12, face = "bold"))

age_pyramid() with aggregated data

If your data are already in counts by age group, you can still use the apyramid package, as shown below.

Let’s say that your dataset already looks like this:

agg_age_data <- linelist %>% 
  group_by(age_group, gender) %>% 
  summarize(cases = dplyr::n())

DT::datatable(agg_age_data)

You can give age_pyramid() the column for the counts like this:

apyramid::age_pyramid(data = agg_age_data,
                      age_group = "age_group",
                      split_by = "gender",
                      count = "cases",      # give the column name for the aggregated counts
                      na.rm = TRUE          # show a bar for patients missing age
                      )

ggplot()

Using ggplot() to build your age pyramid allows more flexibility, but requires more effort and understanding of how ggplot() works.

Constructing the plot

The first thing to understand is that initially the age groups are on the x-axis. You are creating a geom_histogram() layer for each of the two genders, one in positive count values and the other in negative count values. Then you are using the coord_flip() command to switch the X and Y axes.

TODO - automate the axis limits…

## make the actual plot
age_sex_pyramid <- linelist %>%
  ggplot(aes(x = age, fill = gender)) +
  geom_histogram(data = filter(linelist, gender == "f"),
                 breaks = age_seq,
                 colour = "white",
                 closed = "left") +
  geom_histogram(data = filter(linelist, gender == "m"),
                 breaks = age_seq,
                 aes(y=..count..*(-1)),
                 color = "white",
                 closed = "left") +
  scale_x_continuous(breaks = age_seq + 2.5, labels = age_lab, limits = c(0, 90)) +
  scale_y_continuous(limits = c(-20, 20), breaks = seq(-20,20,1), labels = abs(seq(-20, 20, 1))) +
  scale_fill_manual(
    values = c("f" = "orange",
               "m" = "darkgreen"),
    labels = c("Female", "Male"),
  ) +
  labs(
    x = "Age group",
    y = "Number of cases",
    fill = NULL,
    caption = glue::glue("Data are from linelist X \n n = {nrow(linelist)} (age or sex missing for {sum(is.na(linelist$gender) | is.na(linelist$age))} cases \nData as of: {format(Sys.Date(), '%d %b %Y')}")) +
  coord_flip() +
  theme(
    panel.grid.major = element_blank(),
    panel.grid.minor = element_blank(),
    panel.background = element_blank(),
    axis.line = element_line(colour = "black"),
    plot.title = element_text(hjust = 0.5), 
    plot.caption = element_text(hjust=0, size=11)) + 
  
  ggtitle(paste0("Age and gender of cases"))
    
print(age_sex_pyramid)

Compare to expected demographics

With the flexibility of ggplot(), you can have a second layer of bars in the background that represent the true population pyramid. This can provide a nice visualization to compare the observed counts with the baseline.

TO DO

# TO DO

# extract population data
pop <- rio::import(here::here("data/RDC_population_polio_2020.xls")) %>%
  mutate_if(is.character, tolower)

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Overview

Nice to use when tracking metrics at many facilities/regions over time

For example:

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Tracking performance

DT::datatable(facility_count_data, rownames = FALSE, filter="top", options = list(pageLength = 5, scrollX=T))

Group the data by week and location, and then make summary calculations:

# Get number & percent of days data reported for, by week
agg_weeks <- facility_count_data %>% 
  mutate(week = as.Date(aweek::week2date(aweek::date2week(data_date, floor_day = T)))) %>% 
  group_by(location_name, week) %>% 
  summarize(n_days = 7,
            n_reports = n(),
            malaria_tot = sum(malaria_tot, na.rm = T),
            n_days_reported = length(unique(data_date)),
            p_days_reported = round(100*(n_days_reported / n_days))) %>% 
  filter(week < as.Date("2019-06-10"))

### Days
agg_days <- facility_count_data %>% 
  
  filter(data_date < as.Date("2019-06-10"))
  

Then we make the plot:

ggplot(agg_weeks, aes(x=week, y=location_name, fill= p_days_reported))+
  geom_tile(colour="white",size=0.2)+
  guides(fill=guide_legend(title="Reporting\nperformance (%)"))+
  labs(x="Week (date of data)",
       y="Facility name",
       title="Percent of days per week that facility reported data",
       subtitle = "52 health facilities, April-May 2019",
       caption = "7-day weeks beginning on Mondays.")+
  scale_fill_gradient(low = "yellow", high = "darkgreen", na.value = "grey80")+
  theme_light()+
  theme(legend.position="right",legend.direction="vertical",
        legend.title=element_text(size=12, face="bold"),
        legend.margin=margin(grid::unit(0,"cm")),
        legend.text=element_text(size=10,face="bold"),
        legend.key.height=grid::unit(0.8,"cm"),
        legend.key.width=grid::unit(0.2,"cm"),
        axis.text.x=element_text(size=12),
        axis.text.y=element_text(vjust=0.2),
        axis.ticks=element_line(size=0.4),
        axis.title=element_text(size=12, face="bold"),
        plot.background=element_blank(),
        panel.border=element_blank(),
        plot.margin=margin(0.7,0.4,0.1,0.2,"cm"),
        plot.title=element_text(hjust=0,size=14,face="bold"))

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Combination analysis

Overview

This analysis plots the frequency of different combinations of values/responses. In this example, we plot the frequency of symptom combinations.

This analysis is often called:
Multiple response analysis Sets analysis Combinations analysis

The first method shown uses the package ggupset, an the second using the package UpSetR.

An example plot is below. Five symptoms are shown. Below each vertical bar is a line and dots indicating the combination of symptoms reflected by the bar above. To the right, horizontal bars reflect the frequency of each individual symptom.

Preparation

View the data

This linelist includes five “yes/no” variables on reported symptoms. We will need to transform these variables a bit to use the ggupset package to make our plot.

View the data (scroll to the right to see the symptoms variables)

DT::datatable(linelist_sym, rownames = FALSE, filter="top", options = list(pageLength = 5, scrollX=T))

Re-format values

We convert the “yes” and “no the the actual symptom name. If”no", we set the value as blank.

# create column with the symptoms named, separated by semicolons
linelist_sym_1 <- linelist_sym %>% 
  
  # convert the "yes" and "no" values into the symptom name itself
  mutate(fever = case_when(fever == "yes" ~ "fever",          # if old value is "yes", new value is "fever"
                           TRUE           ~ NA_character_),   # if old value is anything other than "yes", the new value is NA
         
         chills = case_when(chills == "yes" ~ "chills",
                           TRUE           ~ NA_character_),
         
         cough = case_when(cough == "yes" ~ "cough",
                           TRUE           ~ NA_character_),
         
         aches = case_when(aches == "yes" ~ "aches",
                           TRUE           ~ NA_character_),
         
         shortness_of_breath = case_when(shortness_of_breath == "yes" ~ "shortness_of_breath",
                           TRUE           ~ NA_character_))

Now we make two final variables:
1. Pasting together all the symptoms of the patient (character variable)
2. Convert the above to class list, so it can be accepted by ggupset to make the plot

linelist_sym_1 <- linelist_sym_1 %>% 
  mutate(
         # combine the variables into one, using paste() with a semicolon separating any values
         all_symptoms = paste(fever, chills, cough, aches, shortness_of_breath, sep = "; "),
         
         # make a copy of all_symptoms variable, but of class "list" (which is required to use ggupset() in next step)
         all_symptoms_list = as.list(strsplit(all_symptoms, "; "))
         )

View the new data. Note the two columns at the end - the pasted combined values, and the list

DT::datatable(linelist_sym, rownames = FALSE, filter="top", options = list(pageLength = 5, scrollX=T))

ggupset

Load required package to make the plot (ggupset)

pacman::p_load(ggupset)

Create the plot:

ggplot(linelist_sym_1,
       aes(x=all_symptoms_list)) +
geom_bar() +
scale_x_upset(reverse = FALSE,
              n_intersections = 10,
              sets = c("fever", "chills", "cough", "aches", "shortness_of_breath")
              )+
  labs(title = "Signs & symptoms",
       subtitle = "10 most frequent combinations of signs and symptoms",
       caption = "Caption here.",
       x = "Symptom combination",
       y = "Frequency in dataset")

More information on ggupset can be found online or offline in the package documentation in your RStudio Help tab.

UpSetR

The UpSetR package allows more customization, but it more difficult to execute:

https://github.com/hms-dbmi/UpSetR read this https://gehlenborglab.shinyapps.io/upsetr/ Shiny App version - you can upload your own data https://cran.r-project.org/web/packages/UpSetR/UpSetR.pdf documentation - difficult to interpret

pacman::p_load(UpSetR)

Convert symptoms variables to 1/0.

# Make using upSetR

linelist_sym_2 <- linelist_sym %>% 
  
  # convert the "yes" and "no" values into the symptom name itself
  mutate(fever = case_when(fever == "yes" ~ 1,          # if old value is "yes", new value is "fever"
                           TRUE           ~ 0),   # if old value is anything other than "yes", the new value is NA
         
         chills = case_when(chills == "yes" ~ 1,
                           TRUE           ~ 0),
         
         cough = case_when(cough == "yes" ~ 1,
                           TRUE           ~ 0),
         
         aches = case_when(aches == "yes" ~ 1,
                           TRUE           ~ 0),
         
         shortness_of_breath = case_when(shortness_of_breath == "yes" ~ 1,
                           TRUE           ~ 0))

Now make the plot, using only the symptom variables. Must designate which “sets” to compare (the names of the symptom variables).
Alternatively use nsets = and order.by = "freq" to only show the top X combinations.

# Make the plot
UpSetR::upset(
  select(linelist_sym_2, fever, chills, cough, aches, shortness_of_breath),
  sets = c("fever", "chills", "cough", "aches", "shortness_of_breath"),
  order.by = "freq",
  sets.bar.color = c("blue", "red", "yellow", "darkgreen", "orange"), # optional colors
  empty.intersections = "on",
  # nsets = 3,
  number.angles = 0,
  point.size = 3.5,
  line.size = 2, 
  mainbar.y.label = "Symptoms Combinations",
  sets.x.label = "Patients with Symptom")

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R projects

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Preparation

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Data preparation steps such as:

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• melting, pivoting, grouping, etc.

sub-tab 1

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sub-tab 2

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Option 1

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Resources

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Advanced RStudio

Overview

rprofiles

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• melting, pivoting, grouping, etc.

sub-tab 1

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sub-tab 2

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Option 1

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Option 1 sub-tab

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Option 2

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Resources

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ggplot tips

Overview

Embed ggplot cheatsheet

Preparation

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• melting, pivoting, grouping, etc.

sub-tab 1

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sub-tab 2

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Highlighting

highlighting one line among many etc gghighlight

Dual axes

Cowplot Complicated method (% 100 * …)

Smart Labeling

ggrepel

Time axes

Dual axes

Adding shapes

Animations

R with interactive console

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Overview

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Preparation

Keep the title of this section as “Preparation”.
Data preparation steps such as:

• Loading dataset
  
• Adding or changing variables
  
• melting, pivoting, grouping, etc.

sub-tab 1

Can be used to separate major steps of data preparation. Re-name as needed

sub-tab 2

Can be used to separate major steps of data preparation. Re-name as needed.

Option 1

This tab can be renamed. This tab should demonstrate execution of the task using recommended package/approach. For example, using a package customized for this task where the execution is simple and fast but perhaps less customizable. For example using incidence package to create an epicurve.

Option 1 sub-tab

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Resources

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sapply/mapply/lapply

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Preparation

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Data preparation steps such as:

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• melting, pivoting, grouping, etc.

sub-tab 1

Can be used to separate major steps of data preparation. Re-name as needed

sub-tab 2

Can be used to separate major steps of data preparation. Re-name as needed.

Option 1

This tab can be renamed. This tab should demonstrate execution of the task using recommended package/approach. For example, using a package customized for this task where the execution is simple and fast but perhaps less customizable. For example using incidence package to create an epicurve.

Option 1 sub-tab

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Option 2

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Resources

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Shiny basics

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Preparation

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• melting, pivoting, grouping, etc.

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sub-tab 2

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flexdashboard

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Resources

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Writing functions

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Preparation

Keep the title of this section as “Preparation”.
Data preparation steps such as:

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• Adding or changing variables
  
• melting, pivoting, grouping, etc.

sub-tab 1

Can be used to separate major steps of data preparation. Re-name as needed

sub-tab 2

Can be used to separate major steps of data preparation. Re-name as needed.

Option 1

This tab can be renamed. This tab should demonstrate execution of the task using recommended package/approach. For example, using a package customized for this task where the execution is simple and fast but perhaps less customizable. For example using incidence package to create an epicurve.

Option 1 sub-tab

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Option 2

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Resources

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Relational databases

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Preparation

Keep the title of this section as “Preparation”.
Data preparation steps such as:

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• Adding or changing variables
  
• melting, pivoting, grouping, etc.

sub-tab 1

Can be used to separate major steps of data preparation. Re-name as needed

sub-tab 2

Can be used to separate major steps of data preparation. Re-name as needed.

Option 1

This tab can be renamed. This tab should demonstrate execution of the task using recommended package/approach. For example, using a package customized for this task where the execution is simple and fast but perhaps less customizable. For example using incidence package to create an epicurve.

Option 1 sub-tab

Sub-tabs if necessary. Re-name as needed.

Option 2

This tab can be re-named. This tab should demonstrate execution of the task a more standard/core package (e.g. ggplot2, or base R) that allows for more flexibility in the output or more package stability. For example, showing how to create an epicurve using ggplot2.

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Resources

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R on network drives

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Overview

Troubleshooting tips, common errors, etc.

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Preparation

Keep the title of this section as “Preparation”.
Data preparation steps such as:

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• Adding or changing variables
  
• melting, pivoting, grouping, etc.

sub-tab 1

Can be used to separate major steps of data preparation. Re-name as needed

sub-tab 2

Can be used to separate major steps of data preparation. Re-name as needed.

Option 1

This tab can be renamed. This tab should demonstrate execution of the task using recommended package/approach. For example, using a package customized for this task where the execution is simple and fast but perhaps less customizable. For example using incidence package to create an epicurve.

Option 1 sub-tab

Sub-tabs if necessary. Re-name as needed.

Option 2

This tab can be re-named. This tab should demonstrate execution of the task a more standard/core package (e.g. ggplot2, or base R) that allows for more flexibility in the output or more package stability. For example, showing how to create an epicurve using ggplot2.

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Resources

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Report factory

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Preparation

Keep the title of this section as “Preparation”.
Data preparation steps such as:

• Loading dataset
  
• Adding or changing variables
  
• melting, pivoting, grouping, etc.

sub-tab 1

Can be used to separate major steps of data preparation. Re-name as needed

sub-tab 2

Can be used to separate major steps of data preparation. Re-name as needed.

Option 1

This tab can be renamed. This tab should demonstrate execution of the task using recommended package/approach. For example, using a package customized for this task where the execution is simple and fast but perhaps less customizable. For example using incidence package to create an epicurve.

Option 1 sub-tab

Sub-tabs if necessary. Re-name as needed.

Option 2

This tab can be re-named. This tab should demonstrate execution of the task a more standard/core package (e.g. ggplot2, or base R) that allows for more flexibility in the output or more package stability. For example, showing how to create an epicurve using ggplot2.

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Resources

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Github and R

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Preparation

Keep the title of this section as “Preparation”.
Data preparation steps such as:

• Loading dataset
  
• Adding or changing variables
  
• melting, pivoting, grouping, etc.

sub-tab 1

Can be used to separate major steps of data preparation. Re-name as needed

sub-tab 2

Can be used to separate major steps of data preparation. Re-name as needed.

Option 1

This tab can be renamed. This tab should demonstrate execution of the task using recommended package/approach. For example, using a package customized for this task where the execution is simple and fast but perhaps less customizable. For example using incidence package to create an epicurve.

Option 1 sub-tab

Sub-tabs if necessary. Re-name as needed.

Option 2

This tab can be re-named. This tab should demonstrate execution of the task a more standard/core package (e.g. ggplot2, or base R) that allows for more flexibility in the output or more package stability. For example, showing how to create an epicurve using ggplot2.

Option 2 sub-tab

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Resources

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Interactive plots

Overview

PLOTLY

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Preparation

Keep the title of this section as “Preparation”.
Data preparation steps such as:

• Loading dataset
  
• Adding or changing variables
  
• melting, pivoting, grouping, etc.

sub-tab 1

Can be used to separate major steps of data preparation. Re-name as needed

sub-tab 2

Can be used to separate major steps of data preparation. Re-name as needed.

Option 1

This tab can be renamed. This tab should demonstrate execution of the task using recommended package/approach. For example, using a package customized for this task where the execution is simple and fast but perhaps less customizable. For example using incidence package to create an epicurve.

Option 1 sub-tab

Sub-tabs if necessary. Re-name as needed.

Option 2

This tab can be re-named. This tab should demonstrate execution of the task a more standard/core package (e.g. ggplot2, or base R) that allows for more flexibility in the output or more package stability. For example, showing how to create an epicurve using ggplot2.

Option 2 sub-tab

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Resources

This tab should stay with the name “Resources”. Links to other online tutorials or resources.

Directory interactions

Overview

Saving files, deleting files, creating folders, interacting with files in a folder, etc Overwriting files in Excel

Preparation

Keep the title of this section as “Preparation”.
Data preparation steps such as:

• Loading dataset
  
• Adding or changing variables
  
• melting, pivoting, grouping, etc.

sub-tab 1

Can be used to separate major steps of data preparation. Re-name as needed

sub-tab 2

Can be used to separate major steps of data preparation. Re-name as needed.

Option 1

This tab can be renamed. This tab should demonstrate execution of the task using recommended package/approach. For example, using a package customized for this task where the execution is simple and fast but perhaps less customizable. For example using incidence package to create an epicurve.

Option 1 sub-tab

Sub-tabs if necessary. Re-name as needed.

Option 2

This tab can be re-named. This tab should demonstrate execution of the task a more standard/core package (e.g. ggplot2, or base R) that allows for more flexibility in the output or more package stability. For example, showing how to create an epicurve using ggplot2.

Option 2 sub-tab

Sub-tabs if necessary. Re-name as needed.

Resources

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Errors & Warnings

Overview

Troubleshooting common errors and warnings

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Preparation

Keep the title of this section as “Preparation”.
Data preparation steps such as:

• Loading dataset
  
• Adding or changing variables
  
• melting, pivoting, grouping, etc.

sub-tab 1

Can be used to separate major steps of data preparation. Re-name as needed

sub-tab 2

Can be used to separate major steps of data preparation. Re-name as needed.

Option 1

This tab can be renamed. This tab should demonstrate execution of the task using recommended package/approach. For example, using a package customized for this task where the execution is simple and fast but perhaps less customizable. For example using incidence package to create an epicurve.

Option 1 sub-tab

Sub-tabs if necessary. Re-name as needed.

Option 2

This tab can be re-named. This tab should demonstrate execution of the task a more standard/core package (e.g. ggplot2, or base R) that allows for more flexibility in the output or more package stability. For example, showing how to create an epicurve using ggplot2.

Option 2 sub-tab

Sub-tabs if necessary. Re-name as needed.

Resources

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